We present a framework for modeling multi-scale processes, and study its performance in the context of stream-flow forecasting in hydrology. Specifically, we propose a novel hierarchical recurrent neural architecture that factorizes the system dynamics at multiple temporal scales and captures their interactions. This framework consists of an inverse and a forward model. The inverse model is used to empirically resolve the system's temporal modes from data (physical model simulations, observed data, or a combination of them from the past), and these states are then used in the forward model to predict streamflow. Experiments on several catchments from the National Weather Service North Central River Forecast Center show that FHNN outperforms standard baselines, including physics-based models and transformer-based approaches. The model demonstrates particular effectiveness in catchments with low runoff ratios and colder climates. We further validate FHNN on the CAMELS (Catchment Attributes and MEteorology for Large-sample Studies), which is a widely used continental-scale hydrology benchmark dataset, confirming consistent performance improvements for 1–7 day streamflow forecasts across diverse hydrological conditions. Additionally, we show that FHNN can maintain accuracy even with limited training data through effective pre-training strategies and training global models.

Abstract When building predictive models for real-world applications, many data are discarded because conventional learning algorithms cannot utilize it, although such data could be very informative. This paper focuses on representation learning using two types of additional data: privileged information (PI) and unlabeled data. PI refers to data available only during training but not at test time. Existing methods transfer the knowledge embedded in PI via supervised mechanisms, making them unable to use unlabeled data. In contrast, self-supervised learning methods can use unlabeled data but cannot learn from PI. While these techniques appear complementary, as we demonstrate, combining them is non-trivial. This paper introduces the privileged information regularized (PIReg) self-supervised learning framework, which utilizes both PI and unlabeled data to learn better representations.

When building predictive models for real-world applications, many data are discarded because conventional learning algorithms cannot utilize it, although such data could be very informative. This paper focuses on representation learning using two types of additional data: privileged information (PI) and unlabeled data. PI refers to data available only during training but not at test time. Existing methods transfer the knowledge embedded in PI via supervised mechanisms, making them unable to use unlabeled data. In contrast, self-supervised learning methods can use unlabeled data but cannot learn from PI. While these techniques appear complementary, as we demonstrate, combining them is non-trivial. This paper introduces the Privileged Information Regularized (PIReg) self-supervised learning framework, which utilizes both PI and unlabeled data to learn better representations.

In recent years, the increasing threat of devastating wildfires has underscored the need for effective prescribed fire management. Process-based computer simulations have traditionally been employed to plan prescribed fires for wildfire prevention. However, even simplified process models are too compute-intensive to be used for real-time decision-making. Traditional ML methods used for fire modeling offer computational speedup but struggle with physically inconsistent predictions, biased predictions due to class imbalance, biased estimates for fire spread metrics (e.g., burned area, rate of spread), and limited generalizability in out-of-distribution wind conditions. This paper introduces a novel machine learning (ML) framework that enables rapid emulation of prescribed fires while addressing these concerns. To overcome these challenges, the framework incorporates domain knowledge in the form of physical constraints, a hierarchical modeling structure to capture the interdependence among variables of interest, and also leverages pre-existing source domain data to augment training data and learn the spread of fire more effectively. Notably, improvement in fire metric (e.g., burned area) estimates offered by our framework makes it useful for fire managers, who often rely on these estimates to make decisions about prescribed burn management. Furthermore, our framework exhibits better generalization capabilities than the other ML-based fire modeling methods across diverse wind conditions and ignition patterns.

Many environmental systems (e.g., hydrology basins) can be modeled as entity whose response (e.g., streamflow) depends on drivers (e.g., weather) conditioned on their characteristics (e.g., soil properties). We introduce Entity-aware Conditional Variational Inference (EA-CVI), a novel probabilistic inverse modeling approach, to deduce entity characteristics from observed driver-response data. EA-CVI infers probabilistic latent representations that can accurately predict response for diverse entities, particularly in out-of-sample few-shot settings. EA-CVI's latent embeddings encapsulate diverse entity characteristics within compact, low-dimensional representations. EA-CVI proficiently identifies dominant modes of variation in responses and offers the opportunity to infer a physical interpretation of the underlying attributes that shape these responses. EA-CVI can also generate new data samples by sampling from the learned distribution, making it useful in zero-shot scenarios. EA-CVI addresses the need for uncertainty estimation, particularly during extreme events, rendering it essential for data-driven decision-making in real-world applications. Extensive evaluations on a renowned hydrology benchmark dataset, CAMELS-GB, validate EA-CVI's abilities.

In many environmental applications, recurrent neural networks (RNNs) are often used to model physical variables with long temporal dependencies. However, due to minibatch training, temporal relationships between training segments within the batch (intra-batch) as well as between batches (inter-batch) are not considered, which can lead to limited performance. Stateful RNNs aim to address this issue by passing hidden states between batches. Since Stateful RNNs ignore intra-batch temporal dependency, there exists a trade-off between training stability and capturing temporal dependency. In this paper, we provide a quantitative comparison of different Stateful RNN modeling strategies, and propose two strategies to enforce both intra- and inter-batch temporal dependency. First, we extend Stateful RNNs by defining a batch as a temporally ordered set of training segments, which enables intra-batch sharing of temporal information. While this approach significantly improves the performance, it leads to much larger training times due to highly sequential training. To address this issue, we further propose a new strategy which augments a training segment with an initial value of the target variable from the timestep right before the starting of the training segment. In other words, we provide an initial value of the target variable as additional input so that the network can focus on learning changes relative to that initial value. By using this strategy, samples can be passed in any order (mini-batch training) which significantly reduces the training time while maintaining the performance. In demonstrating the utility of our approach in hydrological modeling, we observe that the most significant gains in predictive accuracy occur when these methods are applied to state variables whose values change more slowly, such as soil water and snowpack, rather than continuously moving flux variables such as streamflow.

This paper proposes a physics-guided machine learning approach that combines machine learning models and physicsbased models to improve the prediction of water flow and temperature in river networks.We first build a recurrent graph network model to capture the interactions among multiple segments in the river network.Then we transfer knowledge from physics-based models to guide the learning of the machine learning model.We also propose a new loss function that balances the performance over different river segments.We demonstrate the effectiveness of the proposed method in predicting temperature and streamflow in a subset of the Delaware River Basin.In particular, the proposed method has brought a 33%/14% accuracy improvement over the state-of-the-art physics-based model and 24%/14% over traditional machine learning models (e.g., LSTM) in temperature/streamflow prediction using very sparse (0.1%) training data.The proposed method has also been shown to produce better performance when generalized to different seasons or river segments with different streamflow ranges.

OBJECTIVE: Hospital-acquired infections (HAIs) are associated with significant morbidity, mortality, and prolonged hospital length of stay. Risk prediction models based on pre- and intraoperative data have been proposed to assess the risk of HAIs at the end of the surgery, but the performance of these models lag behind HAI detection models based on postoperative data. Postoperative data are more predictive than pre- or interoperative data since it is closer to the outcomes in time, but it is unavailable when the risk models are applied (end of surgery). The objective is to study whether such data, which is temporally unavailable at prediction time (TUP) (and thus cannot directly enter the model), can be used to improve the performance of the risk model. MATERIALS AND METHODS: An extensive array of 12 methods based on logistic/linear regression and deep learning were used to incorporate the TUP data using a variety of intermediate representations of the data. Due to the hierarchical structure of different HAI outcomes, a comparison of single and multi-task learning frameworks is also presented. RESULTS AND DISCUSSION: The use of TUP data was always advantageous as baseline methods, which cannot utilize TUP data, never achieved the top performance. The relative performances of the different models vary across the different outcomes. Regarding the intermediate representation, we found that its complexity was key and that incorporating label information was helpful. CONCLUSIONS: Using TUP data significantly helped predictive performance irrespective of the model complexity.

Major societal and environmental challenges involve complex systems that have diverse multi-scale interacting processes. Consider, for example, how droughts and water reserves affect crop production and how agriculture and industrial needs affect water quality and availability. Preventive measures, such as delaying planting dates and adopting new agricultural practices in response to changing weather patterns, can reduce the damage caused by natural processes. Understanding how these natural and human processes affect one another allows forecasting the effects of undesirable situations and study interventions to take preventive measures. For many of these processes, there are expert models that incorporate state-of-the-art theories and knowledge to quantify a system's response to a diversity of conditions. A major challenge for efficient modeling is the diversity of modeling approaches across disciplines and the wide variety of data sources available only in formats that require complex conversions. Using expert models for particular problems requires integration of models with third-party data as well as integration of models across disciplines. Modelers face significant heterogeneity that requires resolving semantic, spatiotemporal, and execution mismatches, which are largely done by hand today and may take more than 2 years of effort. We are developing a modeling framework that uses artificial intelligence (AI) techniques to reduce modeling effort while ensuring utility for decision making. Our work to date makes several innovative contributions: (1) an intelligent user interface that guides analysts to frame their modeling problem and assists them by suggesting relevant choices and automating steps along the way; (2) semantic metadata for models, including their modeling variables and constraints, that ensures model relevance and proper use for a given decision-making problem; and (3) semantic representations of datasets in terms of modeling variables that enable automated data selection and data transformations. This framework is implemented in the MINT (Model INTegration) framework, and currently includes data and models to analyze the interactions between natural and human systems involving climate, water availability, agricultural production, and markets. Our work to date demonstrates the utility of AI techniques to accelerate modeling to support decision-making and uncovers several challenging directions for future work.

Diseases can show different courses of progression even when patients share the same risk factors. Recent studies have revealed that the use of trajectories, the order in which diseases manifest throughout life, can be predictive of the course of progression. In this study, we propose a novel computational method for learning disease trajectories from EHR data. The proposed method consists of three parts: first, we propose an algorithm for extracting trajectories from EHR data; second, three criteria for filtering trajectories; and third, a likelihood function for assessing the risk of developing a set of outcomes given a trajectory set. We applied our methods to extract a set of disease trajectories from Mayo Clinic EHR data and evaluated it internally based on log-likelihood, which can be interpreted as the trajectories' ability to explain the observed (partial) disease progressions. We then externally evaluated the trajectories on EHR data from an independent health system, M Health Fairview. The proposed algorithm extracted a comprehensive set of disease trajectories that can explain the observed outcomes substantially better than competing methods and the proposed filtering criteria selected a small subset of disease trajectories that are highly interpretable and suffered only a minimal (relative 5%) loss of the ability to explain disease progression in both the internal and external validation.

In this paper, we introduce a new paradigm that combines scientific knowledge within process-based models and machine learning models to advance scientific discovery in many physical systems. We will describe how to incorporate physical knowledge in real-world dynamical systems as additional constraints for training machine learning models and how to leverage the hidden knowledge encoded by existing process-based models. We evaluate this approach on modeling lake water temperature and demonstrate its superior performance using limited training data and the improved generalizability to different scenarios.

In many domains, there is significant interest in capturing novel relationships between time series that represent activities recorded at different nodes of a highly complex system. In this paper, we introduce multipoles, a novel class of linear relationships between more than two time series. A multipole is a set of time series that have strong linear dependence among themselves, with the requirement that each time series makes a significant contribution to the linear dependence. We demonstrate that most interesting multipoles can be identified as cliques of negative correlations in a correlation network. Such cliques are typically rare in a real-world correlation network, which allows us to find almost all multipoles efficiently using a clique-enumeration approach. Using our proposed framework, we demonstrate the utility of multipoles in discovering new physical phenomena in two scientific domains: climate science and neuroscience. In particular, we discovered several multipole relationships that are reproducible in multiple other independent datasets and lead to novel domain insights.

Our aging population increasingly suffers from multiple chronic diseases simultaneously, necessitating the comprehensive treatment of these conditions. Finding the optimal set of drugs for a combinatorial set of diseases is a combinatorial pattern exploration problem. Association rule mining is a popular tool for such problems, but the requirement of health care for finding causal, rather than associative, patterns renders association rule mining unsuitable. To address this issue, we propose a novel framework based on the Rubin-Neyman causal model for extracting causal rules from observational data, correcting for a number of common biases. Specifically, given a set of interventions and a set of items that define subpopulations (e.g., diseases), we wish to find all subpopulations in which effective intervention combinations exist and in each such subpopulation, we wish to find all intervention combinations such that dropping any intervention from this combination will reduce the efficacy of the treatment. A key aspect of our framework is the concept of closed intervention sets which extend the concept of quantifying the effect of a single intervention to a set of concurrent interventions. Closed intervention sets also allow for a pruning strategy that is strictly more efficient than the traditional pruning strategy used by the Apriori algorithm. To implement our ideas, we introduce and compare five methods of estimating causal effect from observational data and rigorously evaluate them on synthetic data to mathematically prove (when possible) why they work. We also evaluated our causal rule mining framework on the Electronic Health Records (EHR) data of a large cohort of 152000 patients from Mayo Clinic and showed that the patterns we extracted are sufficiently rich to explain the controversial findings in the medical literature regarding the effect of a class of cholesterol drugs on Type-II Diabetes Mellitus (T2DM).

We propose a nonlinear manifold learning technique based on deep convolutional autoencoders that is appropriate for model order reduction of physical systems in complex geometries. Convolutional neural networks have proven to be highly advantageous for compressing data arising from systems demonstrating a slow-decaying Kolmogorov $n$-width. However, these networks are restricted to data on structured meshes. Unstructured meshes are often required for performing analyses of real systems with complex geometry. Our custom graph convolution operators based on the available differential operators for a given spatial discretization effectively extend the application space of deep convolutional autoencoders to systems with arbitrarily complex geometry that are typically discretized using unstructured meshes. We propose sets of convolution operators based on the spatial derivative operators for the underlying spatial discretization, making the method particularly well suited to data arising from the solution of partial differential equations. We demonstrate the method using examples from heat transfer and fluid mechanics and show better than an order of magnitude improvement in accuracy over linear methods.

Data science models, although successful in a number of commercial domains, have had limited applicability in scientific problems involving complex physical phenomena. Theory-guided data science (TGDS) is an emerging paradigm that aims to leverage the wealth of scientific knowledge for improving the effectiveness of data science models in enabling scientific discovery. The overarching vision of TGDS is to introduce scientific consistency as an essential component for learning generalizable models. Further, by producing scientifically interpretable models, TGDS aims to advance our scientific understanding by discovering novel domain insights. Indeed, the paradigm of TGDS has started to gain prominence in a number of scientific disciplines such as turbulence modeling, material discovery, quantum chemistry, bio-medical science, bio-marker discovery, climate science, and hydrology. In this paper, we formally conceptualize the paradigm of TGDS and present a taxonomy of research themes in TGDS. We describe several approaches for integrating domain knowledge in different research themes using illustrative examples from different disciplines. We also highlight some of the promising avenues of novel research for realizing the full potential of theory-guided data science.

Electronic Health Records (EHRs) consists of patient information such as demographics, medications, laboratory test results, diagnosis codes and procedures. Mining EHRs could lead to improvement in patient healthcare management as EHRs contain detailed information related to disease prognosis for large patient populations. We hypothesize that a patient's condition does not deteriorate at random, the trajectories, sequences in which diseases appear in a patient, are determined by a finite number of underlying disease mechanisms. In this work, we exploit this idea by predicting a patient's risk of mortality in the context of the metabolic syndrome by assessing which of many available trajectories a patient is following and progression along this trajectory. Implementing this idea required innovative enhancements both for the study design and also for the fitting algorithm. We propose a forensic-style study design, which aligns patients on last follow-up and measures time backwards. We modify the time-dependent covariate Cox proportional hazards model to better capture coefficients of covariate that follow a particular temporal sequence, such as trajectories. Knowledge extracted from such analysis can lead to personalized treatments, thereby forming the basis for future trajectory-centered guidelines.

The focus of this paper is to address the problem of discovering groups of time series that share similar behavior in multiple small intervals of time. This problem has two characteristics: i) There are exponentially many combinations of time series that needs to be explored to find these groups, ii) The groups of time series of interest need to have similar behavior only in some subsets of the time dimension. We present an Apriori based approach to address this problem. We evaluate it on a synthetic dataset and demonstrate that our approach can directly find all groups of intermittently correlated time series without finding spurious groups unlike other alternative approaches that find many spurious groups. We also demonstrate, using a neuroimaging dataset, that groups of intermittently coherent time series discovered by our approach are reproducible on independent sets of time series data. In addition, we demonstrate the utility of our approach on an S&P 500 stocks data set.

Mining patterns from electronic health-care records (EHR) can potentially lead to better and more cost-effective treatments. We aim to find the groups of ICD-9 diagnosis codes from EHRs that can predict the improvement of urinary incontinence of home health care (HHC) patients and also are interpretable to domain experts. In this paper, we propose two approaches for increasing the interpretability of the obtained groups of ICD-9 codes. First, we incorporate prior information available from clinical domain knowledge using the clinical classification system (CCS). Second, we incorporate additional types of clinical information for the same patients, such as demographic, behavioral, physiological, and psycho-social variables available from survey questions during the hospital visits. Finally, we develop a hybrid framework that can combine both prior information and the data-driven clinical information in the predictive model framework. Our results obtained from a large-scale EHR data set show that the hybrid framework enhances clinical interpretability as compared to the baseline model obtained from ICD-9 codes only, while achieving almost the same predictive capability.

Discriminative patterns can provide valuable insights into data sets with class labels, that may not be available from the individual features or the predictive models built using them. Most existing approaches work efficiently for sparse or low-dimensional data sets. However, for dense and high-dimensional data sets, they have to use high thresholds to produce the complete results within limited time, and thus, may miss interesting low-support patterns. In this paper, we address the necessity of trading off the completeness of discriminative pattern discovery with the efficient discovery of low-support discriminative patterns from such data sets. We propose a family of antimonotonic measures named SupMaxKthat organize the set of discriminative patterns into nested layers of subsets, which are progressively more complete in their coverage, but require increasingly more computation. In particular, the member of SupMaxK with K = 2, named SupMaxPair, is suitable for dense and high-dimensional data sets. Experiments on both synthetic data sets and a cancer gene expression data set demonstrate that there are low-support patterns that can be discovered using SupMaxPair but not by existing approaches. Furthermore, we show that the low-support discriminative patterns that are only discovered using SupMaxPair from the cancer gene expression data set are statistically significant and biologically relevant. This illustrates the complementarity of SupMaxPairXo existing approaches for discriminative pattern discovery. The codes and data set for this paper are available at http://vk.cs.umn.edu/SMP/.

GOPHER (Global Observation of Planetary Health and Ecosystem Resources) is a collection of data mining algorithms for detecting global land use and land cover change that builds on a decade of research on spatio temporal data mining at the University of Minnesota; the GOPHER approach to analyzing remote sensing imagery provides such a solution by providing rapid, inexpensive, robust, scalable, and precise detection of land use change. GOPHER algorithms are able to detect historical changes with high precision, and more recent changes with reasonable precision in as little as 8 weeks after changes occur (this is due to a combination of remote sensing data availability and modeling needs). This paper outlines the GOPHER approach and provides some illustrative results to demonstrate its utility.

Pressure dipoles are important long distance climate phenomena (teleconnection) characterized by pressure anomalies of opposite polarity appearing at two different locations at the same time. Such dipoles have proven important for understanding and explaining the variability in climate in many regions of the world, e.g., the El Niño climate phenomenon is known to be responsible for precipitation and temperature anomalies worldwide. This paper presents a novel approach for dipole discovery that outperforms existing state of the art algorithms. Our approach is based on a climate anomaly network that is constructed using the correlation of time series of climate variables at all the locations on the Earth. One novel aspect of our approach to the analysis of such networks is a careful treatment of negative correlations, whose proper consideration is critical for finding dipoles. Another key insight provided by our work is the importance of modeling the time dependent patterns of the dipoles in order to better capture the impact of important climate phenomena on land. The results presented in this paper show that these innovations allow our approach to produce better results than previous approaches in terms of matching existing climate indices with high correlation and capturing the impact of climate indices on land.

Forests are a critical component of the planet's ecosystem. Unfortunately, there has been significant degradation in forest cover over recent decades as a result of logging, conversion to crop, plantation, and pasture land, or disasters (natural or man made) such as forest fires, floods, and hurricanes. As a result, significant attention is being given to the sustainable use of forests. A key to effective forest management is quantifiable knowledge about changes in forest cover. This requires identification and characterization of changes and the discovery of the relationship between these changes and natural and anthropogenic variables. In this article, we present our preliminary efforts and achievements in addressing some of these tasks along with the challenges and opportunities that need to be addressed in the future. At a higher level, our goal is to provide an overview of the exciting opportunities and challenges in developing and applying data mining approaches to provide critical information for forest and land use management.

Massive degradation in forest cover over recent decades caused by natural and human activities has made ability to detect changes in forest cover of critical importance. This paper provides a brief overview of our research on identifying changes in forest cover.

The discovery of biclusters, which denote groups of items that show coherent values across a subset of all the transactions in a data set, is an important type of analysis performed on real-valued data sets in various domains, such as biology. Several algorithms have been proposed to find different types of biclusters in such data sets. However, these algorithms are unable to search the space of all possible biclusters exhaustively. Pattern mining algorithms in association analysis also essentially produce biclusters as their result, since the patterns consist of items that are supported by a subset of all the transactions. However, a major limitation of the numerous techniques developed in association analysis is that they are only able to analyze data sets with binary and/or categorical variables, and their application to real-valued data sets often involves some lossy transformation such as discretization or binarization of the attributes. In this paper, we propose a novel association analysis framework for exhaustively and efficiently mining "range support" patterns from such a data set. On one hand, this framework reduces the loss of information incurred by the binarization- and discretization-based approaches, and on the other, it enables the exhaustive discovery of coherent biclusters. We compared the performance of our framework with two standard biclustering algorithms through the evaluation of the similarity of the cellular functions of the genes constituting the patterns/biclusters derived by these algorithms from microarray data. These experiments show that the real-valued patterns discovered by our framework are better enriched by small biologically interesting functional classes. Also, through specific examples, we demonstrate the ability of the RAP framework to discover functionally enriched patterns that are not found by the commonly used biclustering algorithm ISA. The source code and data sets used in this paper, as well as the supplementary material, are available at http://www.cs.umn.edu/vk/gaurav/rap.

Even after an experimentally prepared gene expression data set has been pre-processed to account for variations in the microarray technology, there may be inconsistencies between the scales of measurements in different conditions. This may happen for reasons such as the accumulation of gene expression data prepared by different laboratories into a single data set. A variety of scaling and transformation methods have been used for addressing these scale inconsistencies in different studies on the analysis of gene expression data sets. However, a quantitative estimation of their relative performance has been lacking. In this paper, we report an extensive evaluation of scaling and transformation methods for their effectiveness with respect to the important problem of protein function prediction. We consider several such commonly used methods for gene expression data, such as z-score scaling, quantile normalization, diff transformation, and two new scaling methods, sigmoid and double sigmoid, that have not been used previously in this domain to the best of our knowledge. We show that the performance of these methods can vary significantly across data sets, but Dsigmoid scaling and z-score transformation generally perform well for the two types of gene expression data, namely temporal and non-temporal, respectively.

The study of land cover change is an important problem in the Earth Science domain because of its impacts on local climate, radiation balance, biogeochemistry, hydrology, and the diversity and abundance of terrestrial species. Most well-known change detection techniques from statistics, signal processing and control theory are not well-suited for the massive high-dimensional spatio-temporal data sets from Earth Science due to limitations such as high computational complexity and the inability to take advantage of seasonality and spatio-temporal autocorrelation inherent in Earth Science data. In our work, we seek to address these challenges with new change detection techniques that are based on data mining approaches. Specifically, in this paper we have performed a case study for a new change detection technique for the land cover change detection problem. We study land cover change in the state of California, focusing on the San Francisco Bay Area and perform an extended study on the entire state. We also perform a comparative evaluation on forests in the entire state. These results demonstrate the utility of data mining techniques for the land cover change detection problem.

Traditional association mining algorithms use a strict definition of support that requires every item in a frequent itemset to occur in each supporting transaction. In real-life datasets, this limits the recovery of frequent itemset patterns as they are fragmented due to random noise and other errors in the data. Hence, a number of methods have been proposed recently to discover approximate frequent itemsets in the presence of noise. These algorithms use a relaxed definition of support and additional parameters, such as row and column error thresholds to allow some degree of "error" in the discovered patterns. Though these algorithms have been shown to be successful in finding the approximate frequent itemsets, a systematic and quantitative approach to evaluate them has been lacking. In this paper, we propose a comprehensive evaluation framework to compare different approximate frequent pattern mining algorithms. The key idea is to select the optimal parameters for each algorithm on a given dataset and use the itemsets generated with these optimal parameters in order to compare different algorithms. We also propose simple variations of some of the existing algorithms by introducing an additional post-processing step. Subsequently, we have applied our proposed evaluation framework to a wide variety of synthetic datasets with varying amounts of noise and a real dataset to compare existing and our proposed variations of the approximate pattern mining algorithms. Source code and the datasets used in this study are made publicly available.

Protein interaction networks are one of the most promising types of biological data for the discovery of functional modules and the prediction of individual protein functions. However, it is known that these networks are both incomplete and inaccurate, i.e., they have spurious edges and lackbiologically valid edges. One way to handle this problem is by transforming the original interaction graph into new graphs that remove spurious edges, add biologically valid ones, and assign reliability scores to the edges constituting the final network. We investigate currently existing methods, as well as propose a robust association analysis-based method for this task. This method is based on the concept of h-confidence, which is a measure that can be used to extract groups of objects having high similarity with each other. Experimental evaluation on several protein interaction data sets show that hyperclique-based transformations enhance the performance of standard function prediction algorithms significantly, and thus have merit.

Removing objects that are noisy is an important goal of data cleaning as noise hinders most types of data analysis. Most existing data cleaning methods focus on removing noise that is the product of low-level data errors that result from an imperfect data collection process, but data objects that are irrelevant or only weakly relevant can also significantly hinder data analysis. Thus, if the goal is to enhance the data analysis as much as possible, these objects should also be considered as noise, at least with respect to the underlying analysis. Consequently, there is a need for data cleaning techniques that remove both types of noise. Because data sets can contain large amounts of noise, these techniques also need to be able to discard a potentially large fraction of the data. This paper explores four techniques intended for noise removal to enhance data analysis in the presence of high noise levels. Three of these methods are based on traditional outlier detection techniques: distance-based, clustering-based, and an approach based on the local outlier factor (LOF) of an object. The other technique, which is a new method that we are proposing, is a hyperclique-based data cleaner (HCleaner). These techniques are evaluated in terms of their impact on the subsequent data analysis, specifically, clustering and association analysis. Our experimental results show that all of these methods can provide better clustering performance and higher quality association patterns as the amount of noise being removed increases, although HCleaner generally leads to better clustering performance and higher quality associations than the other three methods for binary data.

In multi-relational databases, a view, which is a context- and content-dependent subset of one or more tables (or other views), is often used to preserve privacy by hiding sensitive information. However, recent developments in data mining present a new challenge for database security even when traditional database security techniques, such as database access control, are employed. This paper presents a data mining framework using semi-supervised learning that demonstrates the potential for privacy leakage in multi-relational databases. Many different types of semi-supervised learning techniques, such as the K-nearest neighbor (KNN) method, can be used to demonstrate privacy leakage. However, we also introduce a new approach to semi-supervised learning, hyperclique pattern based semi-supervised learning (HPSL), which differs from traditional semi-supervised learning approaches in that it considers the similarity among groups of objects instead of only pairs of objects. Our experimental results show that both the KNN and HPSL methods have the ability to compromise database security, although HPSL is better at this privacy violation than the KNN method.

In this paper, we explore extending association analysis to non-traditional types of patterns and nonbinary data by generalizing the notion of confidence. The key idea is to regard confidence as a measure of the extent to which the strength of one association pattern provides information about the strength of another. This approach provides a framework that encompasses the traditional concept of confidence as a special case and can be used as the basis for designing a variety of new confidence measures. Besides discussing such confidence measures, we provide examples that illustrate the potential usefulness of a generalized notion of confidence. In particular, we describe an approach to defining confidence for error tolerant itemsets that preserves the interpretation of confidence as a conditional probability and derive a confidence measure for continuous data that agrees with the standard confidence measure when applied to binary transaction data.

This paper introduces support envelopes---a new tool for analyzing association patterns---and illustrates some of their properties, applications, and possible extensions. Specifically, the support envelope for a transaction data set and a specified pair of positive integers (m,n) consists of the items and transactions that need to be searched to find any association pattern involving m or more transactions and n or more items. For any transaction data set with M transactions and N items, there is a unique lattice of at most M*N support envelopes that captures the structure of the association patterns in that data set. Because support envelopes are not encumbered by a support threshold, this support lattice provides a complete view of the association structure of the data set, including association patterns that have low support. Furthermore, the boundary of the support lattice---the support boundary---has at most min(M,N) envelopes and is especially interesting since it bounds the maximum sizes of potential association patterns---not only for frequent, closed, and maximal itemsets, but also for patterns, such as error-tolerant itemsets, that are more general. The association structure can be represented graphically as a two-dimensional scatter plot of the (m,n) values associated with the support envelopes of the data set, a feature that is useful in the exploratory analysis of association patterns. Finally, the algorithm to compute support envelopes is simple and computationally efficient, and it is straightforward to parallelize the process of finding all the support envelopes.

The goal of this paper is to show that generalizing the notion of support can be useful in extending association analysis to non-traditional types of patterns and non-binary data. To that end, we describe a framework for generalizing support that is based on the simple, but useful observation that support can be viewed as the composition of two functions: a function that evaluates the strength or presence of a pattern in each object (transaction) and a function that summarizes these evaluations with a single number. A key goal of any framework is to allow people to more easily express, explore, and communicate ideas, and hence, we illustrate how our support framework can be used to describe support for a variety of commonly used association patterns, such as frequent itemsets, general Boolean patterns, and error-tolerant itemsets. We also present two examples of the practical usefulness of generalized support. One example shows the usefulness of support functions for continuous data. Another example shows how the hyperclique pattern---an association pattern originally defined for binary data---can be extended to continuous data by generalizing a support function.

This paper describes a new approach for clustering—pattern preserving clustering—which produces more easily interpretable and usable clusters. This approach is motivated by the following observation: while there are usually strong patterns in the data—patterns that may be key for the analysis and description of the data—these patterns are often split among different clusters by current clustering approaches. This is, perhaps, not surprising, since clustering algorithms have no built in knowledge of these patterns and may often have goals that are in conflict with preserving patterns, e.g., minimize the distance of points to their nearest cluster centroids. Also, patterns are typically overlapping, i.e., may involve some of the same objects, and if the clustering algorithm produces disjoint clusters, then some patterns must be split when the objects are clustered. In this paper we describe a technique for pattern preserving clustering that first finds patterns composed of tightly connected groups of objects or attributes and then, starting from these patterns, performs agglomerative clustering using the Group Average (UPGMA) technique. We present the results of some experiments on document data that compare our approach, HIerarchical Clustering with PAttern Preservation (HICAP), to two other clustering techniques: bisecting K-means and traditional UPGMA. These results show that, despite the extra constraint of pattern preservation, HICAP has performance very much like traditional UPGMA with respect to the cluster evaluation criteria of entropy and F-measure. More importantly, we also illustrate how patterns, if preserved, can aid cluster interpretation.

To analyze the effect of the oceans and atmosphere on land climate, Earth Scientists have developed climate indices, which are time series that summarize the behavior of selected regions of the Earth’s oceans and atmosphere. In the past, Earth scientists have used observation and, more recently, eigenvalue analysis techniques, such as principal components analysis (PCA) and singular value decomposition (SVD), to discover climate indices. However, eigenvalue techniques are only useful for finding a few of the strongest signals. Furthermore, they impose a condition that all discovered signals must be orthogonal to each other, making it difficult to attach a physical interpretation to them. This paper presents an alternative clustering-based methodology for the discovery of climate indices that overcomes these limitations and is based on clusters that represent regions with relatively homogeneous behavior. The centroids of these clusters are time series that summarize the behavior of the ocean or atmosphere in those regions. Some of these centroids correspond to known climate indices and provide a validation of our methodology; other centroids are variants of known indices that may provide better predictive power for some land areas; and still other indices may represent potentially new Earth science phenomena. Finally, we show that cluster based indices generally outperform SVD derived indices, both in terms of area weighted correlation and direct correlation with the known indices.

Finding clusters in data, especially high dimensional data, is challenging when the clusters are of widely differing shapes, sizes, and densities, and when the data contains noise and outliers. We present a novel clustering technique that addresses these issues. Our algorithm first finds the nearest neighbors of each data point and then redefines the similarity between pairs of points in terms of how many nearest neighbors the two points share. Using this definition of similarity, our algorithm identifies core points and then builds clusters around the core points. The use of a shared nearest neighbor definition of similarity alleviates problems with varying densities and high dimensionality, while the use of core points handles problems with shape and size. While our algorithm can find the “dense” clusters that other clustering algorithms find, it also finds clusters that these approaches overlook, i.e., clusters of low or medium density which represent relatively uniform regions “surrounded” by non-uniform or higher density areas. We experimentally show that our algorithm performs better than traditional methods (e.g., K-means, DBSCAN, CURE) on a variety of data sets: KDD Cup ‘99 network intrusion data, NASA Earth science time series data, and two-dimensional point sets. The run-time complexity of our technique is O(n2) if the similarity matrix has to be constructed. However, we discuss a number of optimizations that allow the algorithm to handle large data sets efficiently.