Social media has become increasingly prevalent in the last few years, not only enabling people to connect with each other by social links, but also providing platforms for people to share information and interact over diverse topics. Rich user-generated information, for example, users’ relationships and daily posts, are often available in most social media service websites. Given such information, a challenging problem is to provide reasonable user and community recommendation for a target user, and consequently, help the target user engage in the daily discussions and activities with his/her friends or like-minded people. In this article, we propose a unified framework of recommending users and communities that utilizes the information in social media. Given a user’s profile or a set of keywords as input, our framework is capable of recommending influential users and topic-cohesive interactive communities that are most relevant to the given user or keywords. With the proposed framework, users can find other individuals or communities sharing similar interests, and then have more interaction with these users or within the communities. We present a generative topic model to discover user-oriented and community-oriented topics simultaneously, which enables us to capture the exact topical interests of users, as well as the focuses of communities. Extensive experimental evaluation and case studies on a dataset collected from Twitter demonstrate the effectiveness of our proposed framework compared with other probabilistic-topic-model-based recommendation methods.

The tremendous growth and popularity of social media platforms like Twitter, Facebook, etc. provides business organizations an opportunity to monitor the feedback from its customers, identify their problems and take corrective measures. In this paper, we describe a system to automatically monitor and analyze customer feedback through various social media platforms like Facebook, Twitter, etc. and detect issues faced by the customers. Business organizations can use this system to engage with their customers and help alleviate the problems faced by them. The system uses statistical event detection techniques for identifying various customer issues. The system offers a batch version as well as real time version of event detection algorithm depending upon the client's requirements. We also describe a few case studies illustrating the utility of our proposed system for business organizations in identifying issues faced by their customers through social media channels.

Access to massive real-time user generated personal information from micro blogging services, such as Twitter and Facebook, has the potential to enable new location-based recommendation and advertising services. However, sparse user profile information and low adoption of per-message geo-coordinate information necessitates development of location detection techniques that exposes a user's location from message content. We propose and evaluate content-based machine learning techniques to a) identify tweets containing a user's location, and, b) categorize a user location into the author's present or future location. Such an approach is advantageous because it a) relies purely on message content, b) can be used to predict a user's future presence at a location, c) relates user locations to some context (activities, trip plans, etc.), and, d) can be used to profile users constantly evolving location. Our experimental evaluation shows that the proposed techniques can identify and categorize user locations from message content with high accuracy. We also extract the time entities associated with a user's future location to show when the user would be at that location. Finally we illustrate the location-based data analytics potential of these techniques on two real-world datasets.

In this paper, we propose a framework of recommending users and communities in social media. Given a user's profile, our framework is capable of recommending influential users and topic-cohesive interactive communities that are most relevant to the given user. In our framework, we present a generative topic model to discover user-oriented and community-oriented topics simultaneously, which enables us to capture the exact topic interests of users, as well as the focuses of communities. Extensive evaluation on a data set obtained from Twitter has demonstrated the effectiveness of our proposed framework compared with other probabilistic topic model based recommendation methods.

To gain competitiveness and sustained growth in the 21st century, most businesses are on a mission to become more customer-centric. In order to succeed in this endeavor, it is crucial not only to synthesize and analyze the VOC (the VO ice of the C ustomer) data (i.e., the feedbacks or requirements raised by customers), but also to quickly turn these data into actionable knowledge. Although there are many technologies being developed in this complex problem space, most existing approaches in analyzing customer requests are ad hoc, time-consuming, error-prone, people-based processes which hardly scale well as the quantity of customer information explodes. This often results in the slow response to customer requests. In this article, in order to mine VOC to extract useful knowledge for the best product or service quality, we develop a hybrid framework that integrates domain knowledge with data-driven approaches to analyze the semi-structured customer requests. The framework consists of capturing functional features, discovering the overlap or correlation among the features, and identifying the evolving feature trend by using the knowledge transformation model. In addition, since understanding the relative importance of the individual customer request is very critical and has a direct impact on the effective prioritization in the development process, we develop a novel semantic enhanced link-based ranking (SELRank) algorithm for relatively rating/ranking both customer requests and products. The framework has been successfully applied on Xerox Office Group Feature Enhancement Requirements (XOG FER) datasets to analyze customer requests.

Multi-way data or tensors are generalizations of matrices. Clustering multi-way data is a very important research topic due to the intrinsic rich structures in real-world datasets. Despite significant progress made on subspace clustering for two-way

Constrained clustering through matrix factorization has been shown to largely improve clustering accuracy by incorporating prior knowledge into the factorization process. Although it has been well studied, none of them deal with constrained multi-way data factorization. Multi-way data or Tensors are encoded as high-order data structures. They can be seen as the generalization of matrices. One typical tensor is multiple two-way data/matrices in different time periods. To the best of our knowledge, this paper is the first work developing two general formulation of constrained nonnegative tensor factorization. An extensive experiment conducts a comparative study on the proposed constrained nonnegative tensor factorization and other state-of-the-art algorithms.

With advancement in science and technology, computing systems are becoming increasingly more complex with a growing number of heterogeneous software and hardware components. They are thus becoming more difficult to monitor, manage, and maintain. Traditional approaches to system management have been largely based on domain experts through a knowledge acquisition solution that translates domain knowledge into operating rules and policies. This process has been well known as cumbersome, labor intensive, and error prone. In addition, traditional approaches for system management are difficult to keep up with the rapidly changing environments. There is a pressing need for automatic and efficient approaches to monitor and manage complex computing systems. In this paper, we propose an integrated data-driven framework for computing system management by acquiring the needed knowledge automatically from a large amount of historical log data. Specifically, we apply text mining techniques to automatically categorize the log messages into a set of canonical categories, incorporate temporal information to improve categorization performance, develop temporal mining techniques to discover the relationships between different events, and take a novel approach called event summarization to provide a concise interpretation of the temporal patterns.

This paper establishes a connection between NMF and PLSA on multi-way data, called NTF and T-PLSA respectively. Two types of T-PLSA models are proven to be equivalent to non-negative PARAFAC and non-negative Tucker3. This paper also shows that by running NTF and T-PLSA alternatively, they can jump out of each other's local minima and achieve a better clustering solution.

Efficient and intelligent music information retrieval is a very important topic of the 21st century. With the ultimate goal of building personal music information retrieval systems, this paper studies the problem of identifying ldquosimilarrdquo artists using features from diverse information sources. In this paper, we first present a clustering algorithm that integrates features from both sources to perform bimodal learning. We then present an approach based on the generalized constraint clustering algorithm by incorporating the instance-level constraints. The algorithms are tested on a data set consisting of 570 songs from 53 albums of 41 artists using artist similarity provided by All Music Guide. Experimental results show that the accuracy of artist similarity identification can be significantly improved.

Clustering multi-way data is a very important research topic due to the intrinsic rich structures in real-world datasets. In this paper, we propose the subspace clustering algorithm on multi-way data, called ASI-T (Adaptive Subspace Iteration on Tensor). ASI-T is a special version of High Order SVD (HOSVD), and it simultaneously performs subspace identification using 2DSVD and data clustering using K-Means. The experimental results on synthetic data and real-world data demonstrate the effectiveness of ASI-T.

We introduce the posterior probabilistic clustering (PPC), which provides a rigorous posterior probability interpretation for Nonnegative Matrix Factorization (NMF) and removes the uncertainty in clustering assignment. Furthermore, PPC is closely related to probabilistic latent semantic indexing (PLSI).

Analyzing three-way data has attracted a lot of attention recently due to the intrinsic rich structures in real-world datasets. The PARATUCKER model has been proposed to combine the axis capabilities of the Parafac model and the structural generality of the Tucker model. However, no algorithms have been developed for fitting the PARATUCKER model. In this paper, we propose TANPT algorithm to solve the PARATUCKER model. We apply the algorithm for temporal relation co-clustering on author-topic evolution. Experiments on DBLP datasets demonstrate its effectiveness.

Given a document collection, it is often desirable to find the core subset of documents focusing on a specific topic. We propose a new algorithm for this task. Document clustering aims at par- titioning the document-term datasets into differ- ent groups by optimizing certain objective func- tions. However, they are not suitable for finding hotspots that are described by a small set of doc- uments with few tightly coupled terms. In this pa- per we propose a novel hotspot finding algorithm, DCC (Dense Concept Clustering) in document collections. DCC can extract distinct small top- ics with most representative documents and words simultaneously. The hotspots are dense bicliques in binary document-word matrices and they can be discovered sequentially one at a time using the generalized Motzkin-Straus formalism. The rep- resentative documents and words are tightly cor- related for concept descriptions. Experiments on real document datasets show the effectiveness of the proposed algorithm.

In system management applications, an overwhelming amount of data are generated and collected in the form of temporal events. While mining temporal event data to discover interesting and frequent patterns has obtained rapidly increasing research efforts, users of the applications are overwhelmed by the mining results. The extracted patterns are generally of large volume and hard to interpret, they may be of no emphasis, intricate and meaningless to non-experts, even to domain experts. While traditional research efforts focus on finding interesting patterns, in this paper, we take a novel approach called event summarization towards the understanding of the seemingly chaotic temporal data. Event summarization aims at providing a concise interpretation of the seemingly chaotic data, so that domain experts may take actions upon the summarized models. Event summarization decomposes the temporal information into many independent subsets and finds well fitted models to describe each subset.

IntClust is a software package for clustering gene-expression data with repeated measurements based on interval data analysis. By utilizing interval data for representing replicated microarray data, IntClust is able to take into account the scopes where replicate microarray data are distributed instead of simple data points. The soft ware package offers several transformation models for interval data representations, supports different extended distance similarity/distance measures for interval data analysis, provides some variations of modified K-means clustering, and presents three popular clustering quality evaluation measures. Our experiments show that IntClust improves the clustering performance of gene-expression microarray data over traditional approaches. The software package is available at http://cadse24.cs.fiu.edu/IntClust

In this paper, we study the problems of motif discovery and gene regulation. First, although the sliding window technique based on profiles or consensus sequences is a standard method for discovering motifs in the genomes with prior knowledge of transcription binding sites in orthologous genes from related organisms, it usually has high computational costs. In this paper, we propose an efficient approximation method employing randomized algorithms to identify motifs. The approximation method can be easily combined with the sliding-window technique for efficient and accurate motif discovery. Second, we mine frequent motif combinations and sequential motif patterns to investigate the regulatory relationships between motifs and provide a better understanding of gene expression, regulation, and transcription

Interval data is described by a group of variables, each of which contains a range of continuous values instead of the traditional single continuous or discrete value. Traditional data analysis simply replaces each interval by its representative (e.g., center or mean) and ignores the structure information of intervals. In this paper, we study the problem of clustering interval data using the modified or extended interval data dissimilarity measures. Our contributions are two-fold. First, we discuss various approaches for measuring the dissimilarities/distances between interval data, investigate the relations among them, and present a comprehensive experimental study on clustering interval data. We show that the extended interval data clustering achieves better performance than traditional ones and produces more meaningful and explanatory results. Second, we propose a two-stage approach for clustering interval data by exploiting the relations between the traditional distances and the modified distances. Experimental results show the effectiveness of our approach

Currently, most research on nonnegative matrix factorization (NMF)focus on 2-factor $X=FG^T$ factorization. We provide a systematicanalysis of 3-factor $X=FSG^T$ NMF. While it unconstrained 3-factor NMF is equivalent to it unconstrained 2-factor NMF, itconstrained 3-factor NMF brings new features to it constrained 2-factor NMF. We study the orthogonality constraint because it leadsto rigorous clustering interpretation. We provide new rules for updating $F,S, G$ and prove the convergenceof these algorithms. Experiments on 5 datasets and a real world casestudy are performed to show the capability of bi-orthogonal 3-factorNMF on simultaneously clustering rows and columns of the input datamatrix. We provide a new approach of evaluating the quality ofclustering on words using class aggregate distribution andmulti-peak distribution. We also provide an overview of various NMF extensions andexamine their relationships.

In this paper, we show that PLSI and NMF optimize the same objective function, although PLSI and NMF are different algorithms as verified by experiments. In addition, we also propose a new hybrid method that runs PLSI and NMF alternatively to achieve better solutions.

For today's business organizations, workflow models play important roles in analyzing the productivity, evaluating the performances and costs, optimizing the business operations, and supporting evolving services and products. Workflow mining, the process of empirically extracting structured process descriptions from a set of real executions, thus has attracted a lot of attention recently. However, there are several challenges that have not been fully addressed in the previous research: (i) How can we mine process models with optional tasks? (ii) How can we efficiently use new available workflow log data to incrementally update pre-existing workflow models or to complete previous partial process models? (iii) How can we compare two different workflow models of similar organizations? In this paper, we present our research efforts to address the above challenges. We present a workflow mining algorithm that is able to mine process models with optional tasks and propose an incremental workflow mining algorithm based on intermediate relationships such as ordering and independence. The intermediate relationships can also be used to facilitate the comparison of two process models. We illustrate our algorithms on example data derived from real world applications.

Traditional approaches to system management have been largely based on domain experts through a knowledge acquisition process that translates domain knowledge into operating rules and policies. This has been well known and experienced as a cumbersome, labor intensive, and error prone process. In addition, this process is difficult to keep up with the rapidly changing environments. In this paper, we will describe our research efforts on establishing an integrated framework for mining system log files for automatic management. In particular, we apply text mining techniques to categorize messages in log files into common situations, improve categorization accuracy by considering the temporal characteristics of log messages, develop temporal mining techniques to discover the relationships between different events, and utilize visualization tools to evaluate and validate the interesting temporal patterns for system management.