Search results for "prototype-based clustering"

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Comparison of Internal Clustering Validation Indices for Prototype-Based Clustering

2017

Clustering is an unsupervised machine learning and pattern recognition method. In general, in addition to revealing hidden groups of similar observations and clusters, their number needs to be determined. Internal clustering validation indices estimate this number without any external information. The purpose of this article is to evaluate, empirically, characteristics of a representative set of internal clustering validation indices with many datasets. The prototype-based clustering framework includes multiple, classical and robust, statistical estimates of cluster location so that the overall setting of the paper is novel. General observations on the quality of validation indices and on t…

Fuzzy clusteringlcsh:T55.4-60.8Computer scienceSingle-linkage clusteringCorrelation clustering02 engineering and technologycomputer.software_genrelcsh:QA75.5-76.95Theoretical Computer Scienceprototype-based clusteringCURE data clustering algorithm020204 information systemsprototype-based clustering; clustering validation index; robust statisticsConsensus clusteringalgoritmit0202 electrical engineering electronic engineering information engineeringlcsh:Industrial engineering. Management engineeringCluster analysisk-medians clusteringta113Numerical Analysisbusiness.industryPattern recognitionDetermining the number of clusters in a data setComputational MathematicsComputingMethodologies_PATTERNRECOGNITIONComputational Theory and Mathematicsrobust statistics020201 artificial intelligence & image processinglcsh:Electronic computers. Computer scienceArtificial intelligenceData miningtiedonlouhintabusinessclustering validation indexcomputerAlgorithms
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Improvements and applications of the elements of prototype-based clustering

2018

Clustering or cluster analysis is an essential part of data mining, machine learning, and pattern recognition. The most popularly applied clustering methods are partitioning-based or prototype-based methods. Prototype-based clustering methods usually have easy implementability and good scalability. These methods, such as K-means clustering, have been used for different applications in various fields. On the other hand, prototype-based clustering methods are typically sensitive to initialization, and the selection of the number of clusters for knowledge discovery purposes is not straightforward. In the era of big data, in high-velocity, ever-growing datasets, which can also be erroneous, outl…

random projectionparallel computingknowledge discoveryclustering initializationminimal learning machinedata miningprototype-based clusteringmachine learningkoneoppiminenbig datarinnakkaiskäsittelyklusterianalyysitiedonlouhintarobust clusteringK-means
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