Search results for "Cluster Analysis"

showing 10 items of 848 documents

The Urban Landscape and the Real Estate Market. Structures and Fragments of the Axiological Tessitura in a Wide Urban Area of Palermo

2016

The proposed study deals with the urban landscape of Palermo and its possible representation from the perspective of the real estate market analysis. Real estate is one of the most significant types of capital asset and the wide range of its possible utilizations makes complex the interpretation of the market phenomena. The multi-layered reality of such a large city (represented through the sample of 500 properties) needs to be articulated into a significant set of sub-markets in order to outline the complexity and to map the distribution of homogeneous groups of properties within the whole city area. The comparison between quality and price within each cluster allows us to elicit the degre…

Cluster analysisUrban landscape Real estate market Data mining Cluster analysis Urban regenerationUrban regenerationSettore ICAR/22 - EstimoUrban landscapeData miningReal estate market
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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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Distance Functions, Clustering Algorithms and Microarray Data Analysis

2010

Distance functions are a fundamental ingredient of classification and clustering procedures, and this holds true also in the particular case of microarray data. In the general data mining and classification literature, functions such as Euclidean distance or Pearson correlation have gained their status of de facto standards thanks to a considerable amount of experimental validation. For microarray data, the issue of which distance function works best has been investigated, but no final conclusion has been reached. The aim of this extended abstract is to shed further light on that issue. Indeed, we present an experimental study, involving several distances, assessing (a) their intrinsic sepa…

Clustering high-dimensional dataFuzzy clusteringSettore INF/01 - Informaticabusiness.industryCorrelation clusteringMachine learningcomputer.software_genrePearson product-moment correlation coefficientRanking (information retrieval)Euclidean distancesymbols.namesakeClustering distance measuressymbolsArtificial intelligenceData miningbusinessCluster analysiscomputerMathematicsDe facto standard
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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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Bayesian versus data driven model selection for microarray data

2014

Clustering is one of the most well known activities in scientific investigation and the object of research in many disciplines, ranging from Statistics to Computer Science. In this beautiful area, one of the most difficult challenges is a particular instance of the model selection problem, i.e., the identification of the correct number of clusters in a dataset. In what follows, for ease of reference, we refer to that instance still as model selection. It is an important part of any statistical analysis. The techniques used for solving it are mainly either Bayesian or data-driven, and are both based on internal knowledge. That is, they use information obtained by processing the input data. A…

Clustering Model selection Bayesian information criterion Akaike information criterion Minimum message length BioinformaticsSettore INF/01 - InformaticaComputer sciencebusiness.industryModel selectionBayesian probabilitycomputer.software_genreMachine learningComputer Science ApplicationsData-drivenDetermining the number of clusters in a data setIdentification (information)Bayesian information criterionData miningArtificial intelligenceAkaike information criterionCluster analysisbusinesscomputer
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