Search results for "data clustering"

showing 10 items of 27 documents

Fast dendrogram-based OTU clustering using sequence embedding

2014

Biodiversity assessment is an important step in a metagenomic processing pipeline. The biodiversity of a microbial metagenome is often estimated by grouping its 16S rRNA reads into operational taxonomic units or OTUs. These metagenomic datasets are typically large and hence require effective yet accurate computational methods for processing.In this paper, we introduce a new hierarchical clustering method called CRiSPy-Embed which aims to produce high-quality clustering results at a low computational cost. We tackle two computational issues of the current OTU hierarchical clustering approach: (1) the compute-intensive sequence alignment operation for building the distance matrix and (2) the …

Brown clusteringCURE data clustering algorithmSingle-linkage clusteringCorrelation clusteringCanopy clustering algorithmData miningBiologyHierarchical clustering of networksCluster analysiscomputer.software_genrecomputerHierarchical clusteringProceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
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SMART: Unique splitting-while-merging framework for gene clustering

2014

© 2014 Fa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Successful clustering algorithms are highly dependent on parameter settings. The clustering performance degrades significantly unless parameters are properly set, and yet, it is difficult to set these parameters a priori. To address this issue, in this paper, we propose a unique splitting-while-merging clustering framework, named "splitting merging awareness tactics" (SMART), which does not require any a priori knowledge of either the number …

Clustering algorithmsMicroarrayslcsh:MedicineGene ExpressionBioinformaticscomputer.software_genreCell SignalingData MiningCluster Analysislcsh:ScienceFinite mixture modelOligonucleotide Array Sequence AnalysisPhysicsMultidisciplinarySMART frameworkConstrained clusteringCompetitive learning modelBioassays and Physiological AnalysisMultigene FamilyCanopy clustering algorithmEngineering and TechnologyData miningInformation TechnologyGenomic Signal ProcessingAlgorithmsResearch ArticleSignal TransductionComputer and Information SciencesFuzzy clusteringCorrelation clusteringResearch and Analysis MethodsClusteringMolecular GeneticsCURE data clustering algorithmGeneticsGene RegulationCluster analysista113Gene Expression Profilinglcsh:RBiology and Life SciencesComputational BiologyCell BiologyDetermining the number of clusters in a data setComputingMethodologies_PATTERNRECOGNITIONSplitting-merging awareness tactics (SMART)Signal ProcessingAffinity propagationlcsh:QGene expressionClustering frameworkcomputer
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Computation Cluster Validation in the Big Data Era

2017

Data-driven class discovery, i.e., the inference of cluster structure in a dataset, is a fundamental task in Data Analysis, in particular for the Life Sciences. We provide a tutorial on the most common approaches used for that task, focusing on methodologies for the prediction of the number of clusters in a dataset. Although the methods that we present are general in terms of the data for which they can be used, we offer a case study relevant for Microarray Data Analysis.

Clustering high-dimensional dataClass (computer programming)Clustering validation measureSettore INF/01 - InformaticaComputer sciencebusiness.industryBig dataInferenceMicroarrays data analysiscomputer.software_genreGap statisticTask (project management)ComputingMethodologies_PATTERNRECOGNITIONCURE data clustering algorithmConsensus clusteringHypothesis testing in statisticClustering Class Discovery in Data Algorithmsb Clustering algorithmFigure of meritConsensus clusteringData miningCluster analysisbusinesscomputer
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GenClust: A genetic algorithm for clustering gene expression data

2005

Abstract Background Clustering is a key step in the analysis of gene expression data, and in fact, many classical clustering algorithms are used, or more innovative ones have been designed and validated for the task. Despite the widespread use of artificial intelligence techniques in bioinformatics and, more generally, data analysis, there are very few clustering algorithms based on the genetic paradigm, yet that paradigm has great potential in finding good heuristic solutions to a difficult optimization problem such as clustering. Results GenClust is a new genetic algorithm for clustering gene expression data. It has two key features: (a) a novel coding of the search space that is simple, …

Clustering high-dimensional dataDNA ComplementaryComputer scienceRand indexCorrelation clusteringOligonucleotidesEvolutionary algorithmlcsh:Computer applications to medicine. Medical informaticscomputer.software_genreBiochemistryPattern Recognition AutomatedBiclusteringOpen Reading FramesStructural BiologyCURE data clustering algorithmConsensus clusteringGenetic algorithmCluster AnalysisCluster analysislcsh:QH301-705.5Molecular BiologyGene expression data Clustering Evolutionary algorithmsOligonucleotide Array Sequence AnalysisModels StatisticalBrown clusteringHeuristicGene Expression ProfilingApplied MathematicsComputational BiologyComputer Science Applicationslcsh:Biology (General)Gene Expression RegulationMutationlcsh:R858-859.7Data miningSequence AlignmentcomputerSoftwareAlgorithmsBMC Bioinformatics
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Data Analysis and Bioinformatics

2007

Data analysis methods and techniques are revisited in the case of biological data sets. Particular emphasis is given to clustering and mining issues. Clustering is still a subject of active research in several fields such as statistics, pattern recognition, and machine learning. Data mining adds to clustering the complications of very large data-sets with many attributes of different types. And this is a typical situation in biology. Some cases studies are also described.

Clustering high-dimensional dataFuzzy clusteringComputer sciencebusiness.industryCorrelation clusteringConceptual clusteringMachine learningcomputer.software_genreComputingMethodologies_PATTERNRECOGNITIONCURE data clustering algorithmConsensus clusteringCanopy clustering algorithmData miningArtificial intelligenceCluster analysisbusinesscomputer
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Structural clustering of millions of molecular graphs

2014

We propose an algorithm for clustering very large molecular graph databases according to scaffolds (i.e., large structural overlaps) that are common between cluster members. Our approach first partitions the original dataset into several smaller datasets using a greedy clustering approach named APreClus based on dynamic seed clustering. APreClus is an online and instance incremental clustering algorithm delaying the final cluster assignment of an instance until one of the so-called pending clusters the instance belongs to has reached significant size and is converted to a fixed cluster. Once a cluster is fixed, APreClus recalculates the cluster centers, which are used as representatives for…

Clustering high-dimensional dataFuzzy clusteringTheoretical computer sciencek-medoidsComputer scienceSingle-linkage clusteringCorrelation clusteringConstrained clusteringcomputer.software_genreComplete-linkage clusteringGraphHierarchical clusteringComputingMethodologies_PATTERNRECOGNITIONData stream clusteringCURE data clustering algorithmCanopy clustering algorithmFLAME clusteringAffinity propagationData miningCluster analysiscomputerk-medians clusteringClustering coefficientProceedings of the 29th Annual ACM Symposium on Applied Computing
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The Three Steps of Clustering In The Post-Genomic Era

2013

This chapter descibes the basic algorithmic components that are involved in clustering, with particular attention to classification of microarray data.

Clustering high-dimensional dataSettore INF/01 - Informaticabusiness.industryCorrelation clusteringPattern recognitioncomputer.software_genreBiclusteringCURE data clustering algorithmClustering Classification Biological Data MiningConsensus clusteringArtificial intelligenceData miningbusinessCluster analysiscomputerMathematics
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Incrementally Assessing Cluster Tendencies with a~Maximum Variance Cluster Algorithm

2003

A straightforward and efficient way to discover clustering tendencies in data using a recently proposed Maximum Variance Clustering algorithm is proposed. The approach shares the benefits of the plain clustering algorithm with regard to other approaches for clustering. Experiments using both synthetic and real data have been performed in order to evaluate the differences between the proposed methodology and the plain use of the Maximum Variance algorithm. According to the results obtained, the proposal constitutes an efficient and accurate alternative.

Clustering high-dimensional datak-medoidsComputer scienceCURE data clustering algorithmSingle-linkage clusteringCanopy clustering algorithmVariance (accounting)Data miningCluster analysiscomputer.software_genrecomputerk-medians clustering
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A Greedy Algorithm for Hierarchical Complete Linkage Clustering

2014

We are interested in the greedy method to compute an hierarchical complete linkage clustering. There are two known methods for this problem, one having a running time of \({\mathcal O}(n^3)\) with a space requirement of \({\mathcal O}(n)\) and one having a running time of \({\mathcal O}(n^2 \log n)\) with a space requirement of Θ(n 2), where n is the number of points to be clustered. Both methods are not capable to handle large point sets. In this paper, we give an algorithm with a space requirement of \({\mathcal O}(n)\) which is able to cluster one million points in a day on current commodity hardware.

CombinatoricsCURE data clustering algorithmSUBCLUNearest-neighbor chain algorithmCorrelation clusteringSingle-linkage clusteringHierarchical clustering of networksGreedy algorithmComplete-linkage clusteringMathematics
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Complex Networked Systems: Convergence Analysis, Dynamic Behaviour, and Security.

Complex networked systems are a modern reference framework through which very dierent systems from far disciplines, such as biology, computer science, physics, social science, and engineering, can be described. They arise in the great majority of modern technological applications. Examples of real complex networked systems include embedded systems, biological networks, large-scale systems such as power generation grids, transportation networks, water distribution systems, and social network. In the recent years, scientists and engineers have developed a variety of techniques, approaches, and models to better understand and predict the behaviour of these systems, even though several research…

Complex Network Data clustering Hegselmann-Krause model Consensus Security Attacks Line Network k-means Opinion Dynamics.Settore ING-INF/04 - Automatica
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