MOTIVATION: Untargeted metabolomics, the comprehensive analysis of small molecules in biological systems, has become an invaluable tool for understanding physiology and metabolism. However, the annotation of metabolomic data is often confounded by the presence of redundant features, which can arise from e.g. multimerization, in-source fragments (ISFs), and adducts. RESULTS: MS1FA uniquely integrates all major annotation approaches for redundant features within a single interactive platform. It combines correlation-based grouping with reliable ISF annotation using MS2 data and operates with MS1 data only, MS2 data only, or both. Additionally, it offers a distinctive method for grouping features based on relational criteria. As the only web-based platform with these capabilities, MS1FA provides easy access and allows users to explore and annotate the feature table interactively, with options to download the results. AVAILABILITY AND IMPLEMENTATION: MS1FA is freely accessible at https://ms1fa.helmholtz-hzi.de. The source code and data are available at https://github.com/RuibingS/MS1FA_RShiny_dashboard and are archived with the DOI 10.5281/zenodo.15118962.

Antimicrobial resistance is a growing global challenge with significant implications for public health. Wastewater surveillance offers a promising approach to monitoring and predicting the dissemination of antibiotic-resistant bacteria and genes (ARGs) in systems like sewer systems. However, current studies often lack integration between the optimization of sampling locations and the dynamic updating of predicted source locations based on continuous measurements. This paper proposes a novel data-driven framework that bridges this gap by combining multi-objective optimization for selecting optimal sampling locations with Bayesian updating for probabilistic source detection. The framework accounts for DNA degradation, sewage dilution and measurement variability to realistically simulate ARG concentrations in hydrosystems. Through iterative updates guided by detected signals at selected sampling locations, the model refines the likelihood of each sub-catchment being the main source of multiple ARGs, enabling accurate source localization. Validation was conducted using two real-world sewer systems under varying structural and sampling constraints. Across 1,000 simulated ARG scenarios and different sampler limits, results show that using only 3 and 5 samplers, the framework can achieve over 80% detection accuracy within four iterations; using 7 and 12 samplers raises accuracy to above 90%, depending on the complexity of the networks. These findings demonstrate the scalability, robustness, and practical applicability of the framework for ARG monitoring and decision support in diverse wastewater systems.

In technical systems the analysis of similar load situations is a promising technique to gain information about the system’s state, its health or wearing. Very often, load situations are challenging to be defined by hand. Hence, these situations need to be discovered as recurrent patterns within multivariate time series data of the system under consideration. Unsupervised algorithms for finding such recurrent patterns in multivariate time series must be able to cope with very large data sets because the system might be observed over a very long time. In our previous work we identified discretization-based approaches to be very interesting for variable length pattern discovery because of their low computing time due to the simplification (symbolization) of the time series. In this paper we propose additional preprocessing steps for symbolic representation of time series aiming for enhanced multivariate pattern discovery. Beyond that we show the performance (quality and computing time) of our algorithms in a synthetic test data set as well as in a real life example with 100 millions of time points. We also test our approach with increasing dimensionality of the time series.

Artificial or simulated data are particularly relevant in tests and benchmarks for machine learning methods, in teaching for exercises and for setting up analysis workflows. They are relevant when real data may not be used for reasons of data protection, or when special distributions or effects should be present in the data to test certain machine learning methods. In this paper a generator for multivariate numerical data with arbitrary marginal distributions and – as far as possible – arbitrary correlations is presented. The data generator is implemented in the open source statistics software R. It can also be used for categorical variables, if data are generated separately for the corresponding characteristics of a categorical variable. Additionally, outliers can be integrated. The use of the data generator is demonstrated with a concrete example.

The LEGaTO project leverages task-based programming models to provide a software ecosystem for Made in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC, balanced with the security and resilience challenges. LEGaTO is an ongoing three-year EU H2020 project started in December 2017.

Pipe bursts and leaks in district heating networks are a problem both for the economic operation and for the supply reliability of the connected consumers. In case of a leakage, pressure and flow rate conditions near the defect change. These changes spread to the entire network within a short period of time and, depending on the size of the leakage, partly lead to drastically changed network conditions. After a leakage is detected, it is necessary to localize the leakage as accurate as possible in order to shut down the affected network segment and maintain the network's stability. This article discusses and compares three different approaches for leakage localization (pressure wave detection, model-based numeric-analytical and machine learning) that exploit different properties of simulation models and sensor information from the real network.

LEGaTO is a three-year EU H2020 project which started in December 2017. The LEGaTO project will leverage task-based programming models to provide a software ecosystem for Made-in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC.

The domain name system (DNS) is often abused by criminals as resilient infrastructure for their network architecture. Examples for malicious activities based on these networks comprise e.g. phishing, click fraud, spam, command and control structure of botnets. Most of the proposed detection methods rely on machine learning based on complex feature sets which require a considerable computational power. This paper investigates the approach of passively monitoring and analyzing DNS traffic in a time efficient manner based on machine learning on a reduced and robust feature set. For the evaluation the full DNS data stream of a regional ISP is used. To enhance the amount of traffic that can be labeled for the training process and reduce the number of false negatives in the case study, this is combined with a semi-manual labeling approach which addresses domains created by Domain-Generation-Algorithms (DGAs). That allows also medium sized, regional service providers to train classifiers with typical DNS traffic and to deploy systems based on the approach proposed here, in the network of organizations as an alternative to cloud services. The evaluation shows that this approach is feasible and prototypes are already deployed. Hence this approach can serve as an important aspect of the internal risk management of organizations.

Cluster analysis is often used to find clusters and algorithms are designed and tuned to find the “right” clusters. Instead of searching for the “best” clustering algorithm, we argue that a clear concept of what the aim of a cluster analysis is and a better understanding of the data – especially based on visualisations – can be more crucial than the search for the right algorithm. In this paper, we revisit a method called dynamic data assigning assessment clustering that was intended both to asses the inherent cluster structure in a data set as well as to find the clusters. Here we extend this algorithm for better visualisation of possible cluster structures and also to validate single clusters that were found by other algorithms. Although this new approach can help to identify clusters, it is supporting tool and not used as a clustering algorithm itself.

UNLABELLED: Receiver operating characteristic (ROC) analysis is usually applied in bioinformatics to evaluate the abilities of biological markers to differentiate between the presence or absence of a disease. It includes the derivation of the useful scalar performance measure area under the ROC curve for binary classification tasks. As real applications often deal with more than two classes, multicategory ROC analysis and the corresponding hypervolume under the manifold (HUM) measure have become a topic of growing interest. To support researchers in carrying out multicategory ROC analysis, we have developed two tools in different programming environments which feature user-friendly, object-oriented and flexible interfaces and enable the user to compute HUM values and plot 2D- and 3D-ROC curves. AVAILABILITY: The software is freely available from our Web site http://public.ostfalia.de/∼klawonn/HUM.htm

The curse of dimensionality, which refers to both the combinatorial explosion in dimensions and the concentration of distances or norms in high dimensions, affects most of the clustering techniques. Recent studies on the concentration of norms suggest the use of a correlation measure instead of distances to more effectively judge (dis)similarity in high dimensions. In this work, based on these observations, we propose a robust rank correlation based clustering method. Specifically, we employ the recently proposed fuzzy gamma rank correlation measure. We show that this intuitively simple algorithm has the following advantages: (i) It requires very few parameters to be set, (ii) the number of clusters need not be apriori known, (iii) while there is an indirect dependence on the underlying distance measure, its makes use of both global and local information, (iv) it can be robust to noise depending on the correlation measure employed and, (v) as it is shown, performs well with high dimensional data. We illustrate the algorithm on some datasets where the traditional Fuzzy C-Means algorithm is known to fail.

MOTIVATION: Mass spectrometry (MS) data are impaired by noise similar to many other analytical methods. Therefore, proteomics requires statistical approaches to determine the reliability of regulatory information if protein quantification is based on ion intensities observed in MS. RESULTS: We suggest a procedure to model instrument and workflow-specific noise behaviour of iTRAQ reporter ions that can provide regulatory information during automated peptide sequencing by LC-MS/MS. The established mathematical model representatively predicts possible variations of iTRAQ reporter ions in an MS data-dependent manner. The model can be utilized to calculate the robustness of regulatory information systematically at the peptide level in so-called bottom-up proteome approaches. It allows to determine the best fitting regulation factor and in addition to calculate the probability of alternative regulations. The result can be visualized as likelihood curves summarizing both the quantity and quality of regulatory information. Likelihood curves basically can be calculated from all peptides belonging to different regions of proteins if they are detected in LC-MS/MS experiments. Therefore, this approach renders excellent opportunities to detect and statistically validate dynamic post-translational modifications usually affecting only particular regions of the whole protein. The detection of known phosphorylation events at protein kinases served as a first proof of concept in this study and underscores the potential for noise models in quantitative proteomics.

International Journal of Uncertainty, Fuzziness and Knowledge-Based SystemsVol. 15, No. 05, pp. v-vii (2007) Special Issue: Fuzzy Methods in Machine Learning and Data MiningNo AccessEDITORIALEYKE HÜLLERMEIER, FRANK KLAWONN, and ANDREAS NÜRNBERGEREYKE HÜLLERMEIERMarburg University, Germany, FRANK KLAWONNUniversity of Applied Sciences Braunschweig/Wolfenbuettel, Germany, and ANDREAS NÜRNBERGERUniversity of Magdeburg, Germanyhttps://doi.org/10.1142/S0218488507004844Cited by:1 Next AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Remember to check out the Most Cited Articles! Check out our titles on Fuzzy Logic & Z-Numbers With a wide range of areas, you're bound to find something you like. FiguresReferencesRelatedDetailsCited By 1A Bibliometric Analysis of the First Twenty-Five Years of the International Journal of Uncertainty, Fuzziness and Knowledge-Based SystemsWanru Wang, Sigifredo Laengle, José M. Merigó, Dejian Yu, Enrique Herrera-Viedma, Manuel J. Cobo, and Bernadette Bouchon-Meunier12 April 2018 | International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol. 26, No. 02 Recommended Vol. 15, No. 05 Metrics History PDF download

A novel neuro-fuzzy approach to nonlinear dimensionality reduction is proposed. The approach is an auto-associative modification of the Neuro-Fuzzy Kolmogorov's Network (NFKN) with a “bottleneck” hidden layer. Two training algorithms are considered. The validity of theoretical results and the advantages of the proposed model are confirmed by an experiment in nonlinear principal component analysis and an application in the visualization of high-dimensional wastewater treatment plant data.

This paper presents different techniques to visualize high-dimensional fuzzy rule bases in relation to the classified data. The degree of membership to influential rules can be visualized for an entire data set. This enables the observer to detect conflicting or error-prone rules as well as misclassified feature vectors. Results are shown on a benchmark data set and on a weather data set that is used to predict flight durations on a major European airport.

Learning a fuzzy classifier from data is a well-known technique in fuzzy data analysis and many learning algorithms have been proposed, typically in the area of neuro-fuzzy systems. All learning algorithms require a number of parameters to be set by the user. These are typically initial fuzzy partitions for all variables and sometimes also the number of fuzzy rules. Especially, for neuro-fuzzy algorithms the initial choice of parameters can be crucial and if ill-chosen may lead to failure of the learning algorithm. Recent trends in data analysis show that automation is an important issue because it helps to provide advanced analytics to users who are no data analysis experts. In order to fully automate a learning algorithm for fuzzy classifiers we preferably need an algorithm that can determine a suitable initial fuzzy partition for the learning algorithm to start with. In this paper we propose such an algorithm that we have implemented to extend the neuro-fuzzy NEFCLASS. NEFCLASS has recently been integrated into an automatic soft computing platform for intelligent data analysis (SPIDA).

Multidimensional scaling provides low-dimensional visualisation of high-dimensional feature vectors. This is a very important step in data preprocessing because it helps the user to appraise which methods to use for further data analysis. But a well known problem with conventional MDS is the quadratic need of space and time. Beside this, a transformation of MDS must be completely recomputed if additional feature vectors have to be considered. The POLARMAP algorithm, presented in this paper, learns a function, similar to NeuroScale, but with lower computational costs, that maps high-dimensional feature vectors to a 2-dimensional feature space. With the obtained function even new feature vectors can be mapped to the target space

Regression problems occur in many data analysis applications. The aim of regression is to approximate a function from which measurements were taken. When considering a regression problem, we have to take a number of aspects into account: how noisy the data are, whether they cover the domain sufficiently in which we want to find the regression function and what kind of regression function we should choose. However, the underlying assumption is always that the data actually are (noisy) samples of a function. In some cases, this might not be true. For instance, when we consider data from a technical process that is controlled by human operators, these operators might use different strategies to reach a particular goal. Even a single operator might not stick to the same strategy all the time. Thus, a dataset, containing a mixture of samples from different strategies, does not represent (noisy) samples from a single function. Therefore, there exists an ambiguity of selecting data from a large dataset for regression problems (RP) to fit a single model. To tackle this problem, an approach is proposed here to detect ambiguities in RP by selecting a subset of data form the total dataset using two TSK models, which work in parallel by sharing the data with each other in every step. The proposed approach is verified with artificial data, and finally utilised to real data of grinding, a manufacturing process used to generate a smooth surfaces on workpieces.

In this paper, we revisit the convergence and optimization properties of fuzzy clustering algorithms, in general, and the fuzzy c-means (FCM) algorithm, in particular. Our investigation includes probabilistic and (a slightly modified implementation of) possibilistic memberships, which will be discussed under a unified view. We give a convergence proof for the axis-parallel variant of the algorithm by Gustafson and Kessel, that can be generalized to other algorithms more easily than in the usual approach. Using reformulated fuzzy clustering algorithms, we apply Banach's classical contraction principle and establish a relationship between saddle points and attractive fixed points. For the special case of FCM we derive a sufficient condition for fixed points to be attractive, allowing identification of them as (local) minima of the objective function (excluding the possibility of a saddle point).

In this paper, we develop an objective function-based clustering algorithm to build fuzzy models of the Takagi-Sugeno (TS) type automatically from data. In contrast to most of the TS models that can be found in the literature, we decided to use very simple input-space partitions and a higher degree of consequence polynomials (quadratic). Only in this way can transparency and interpretability be guaranteed. We also show how to derive linguistic labels for the polynomials found by the algorithm.

We introduce an objective function-based fuzzy clustering technique that assigns one influence parameter to each single data variable for each cluster. Our method is not only suited to detect structures or groups of data that are not uniformly distributed over the structure's single domains, but gives also information about the influence of individual variables on the detected groups. In addition, our approach can be seen as a generalization of the well-known fuzzy c-means clustering algorithm.

MOTIVATION: Presently available programs for the recognition of potential transcription factor binding sites in genomic sequences generally yield a huge amount of output. These output lists have to be filtered to obtain biologically significant elements, which is highly laborious work to be done manually. RESULTS: We developed a strategy for systematic verification and improvement of the underlying profiles, and for their contextual analysis by a fuzzy clustering approach using non-redundant libraries of search profiles as a prerequisite. AVAILABILITY: The tools mentioned in the paper are available upon request. CONTACT: [email protected]

Objective function-based fuzzy clustering aims at finding a fuzzy partition by optimizing a function that evaluates a (fuzzy) assignment of a given data set to clusters that are characterized by a set of parameters, the so-called prototypes. The iterative optimization technique usually requires the objective function not only to be differentiable, but prefers also an analytical solution for the equations of necessary conditions for local optima. Evolutionary algorithms are known to be an alternative robust optimization technique which is applicable to quite general forms of objective functions. We investigate the possibility of making use of evolutionary algorithms in fuzzy clustering. Our experiments and theoretical investigations show that the application of evolutionary algorithms to shell clustering, where the clusters are in the form of geometric contours, is not very promising due to the shape of the objective function, whereas they can be helpful in finding solid clusters that are not smooth, for example, rectangles or cubes. These types of clusters play an important role in fuzzy rule extraction from data. © 1998 John Wiley & Sons, Inc.

The way engineers use fuzzy control in real world applications is often not coherent with an understanding of the control rules as logical statements or implications. In most cases fuzzy control can be seen as an interpolation of a partially specified control function in a vague environment, which reflects the indistinguishability of measurements or control values. In this paper the authors show that equality relations turn out to be the natural way to represent such vague environments and they develop suitable interpolation methods to obtain a control function. As a special case of our approach the authors obtain Mamdani's model and can justify the inference mechanism in this model and the use of triangular membership functions not only for the reason of simplified computations, and they can explain why typical fuzzy partitions are preferred. The authors also obtain a criterion for reasonable defuzzification strategies. The fuzzy control methodology introduced in this paper has been applied successfully in a case study of engine idle speed control for the Volkswagen Golf GTI.>

Within the theory of belief functions Dempster's rule of combination is of particular importance for the integration of different bodies of evidence. In this article a set of axioms is presented that uniquely determines this rule. As the axioms reflect just the intuitive idea of partially moveable evidence masses the presented results emphasize the unicity of Dempster's rule. © 1992 John Wiley & Sons. Inc.

A foundational development of propositional fuzzy logic programs is presented. Fuzzy logic programs are structured knowledge bases including uncertainties in rules and facts. The precise specifications of uncertainties have a great influence on the performance of the knowledge base. It is shown how fuzzy logic programs can be transformed to neural networks, where adaptations of uncertainties in the knowledge base increase the reliability of the program and are carried out automatically.