6533b852fe1ef96bd12aad01
RESEARCH PRODUCT
Comparison of Internal Clustering Validation Indices for Prototype-Based Clustering
Tommi KärkkäinenSusanne JauhiainenJoonas Hämäläinensubject
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 indexcomputerdescription
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 the behavior of different variants of clustering algorithms will be given. peerReviewed
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-09-06 | Algorithms |