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DSS 第九組期末報告 7.4 P.401~P.408. 組員: 93156204 盧宗佑 93156206 王雅玲 93156208 許韶玲 93156219 詹伯為. DSS 第九組期末報告. 7-4 Partitioning Methods. DSS 第九組期末報告. 目錄. Cluster Partitional clustering algorithm Partitioning algorithm K-Means K-Medoids 大型資料庫處理 綜合比較 總結. 目錄. Cluster - PowerPoint PPT Presentation
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DSS
7.4 P.401~P.408 93156204 93156206 93156208 93156219
DSS*
DSS
7-4 PARTITIONING METHODS DSS*
ClusterPartitional clustering algorithmPartitioning algorithmK-MeansK-Medoids*
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ClusterPartitional clustering algorithmPartitioning algorithmK-MeansK-Medoids*
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CLUSTERclustercluster
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CLUSTERDSS*
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ClusterPartitional clustering algorithmPartitioning algorithmK-MeansK-Medoids*
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PARTITIONAL CLUSTERING ALGORITHM
(Partitioning Algorithm)
(Hierarchical Algorithm)
(Density-Based Algorithm) *
PARTITIONING ALGORITHMK (partition)(cluster) (partitioning criterion) (similarity function)
K-MeansK-Medoids (heuristic) *
ClusterPartitional clustering algorithmPartitioning algorithmK-MeansK-Medoids*
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K-MEANS1967J.B.MacQueen
K-means
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(spherical-shaped) data mining
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K-MEANS k(cluster)
k
k
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K-MEANS Input K- D-nOutputK
Method:nK 1n ()*
K-MEANS 4. 3 ((cluster) )5.
() square-error criterion()
miipiCii*
EXAMPLE1. 6 INSTANCES, 2 ATTRIBUTES, 2 CLUSTERS2. RANDOMLY SELECT INSTANCE 1&3 IN INITIAL*
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K-MEANS k- (scalable)
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K-MEANS1.Hierarchical agglomerationDetermines the number of cluster and finds an initial clustering , and the then use iterative relocation to improve the clustering.
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K-MEANS()2.K-modesExtend the k-means paradigm to cluster categorical data by replacing the means of cluster with modes. using new dissimilarity measure.Using new dissimilarity measures to deal with categorical object and a frequency-based method to update modes of clusters.*
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K-MEANS()3.EM(Expectation-Maximization)Each object is assigned to each cluster according to a weight representing its probability of membership.
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ClusterPartitional clustering algorithmPartitioning algorithmK-MeansK-Medoids*
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K-MEDOIDS METHOD k-k-k-k-k-E
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ABSOLUTE-ERROR CRITERION
Eabsolute errorCjclusterPCjMiCjobject*
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K-MEDOIDSk-k-k-k-k-E(Partitioning Around Medoids, PAM) *
PAMPAM Kaufman and Rousseeuw
K-means
medoidmean
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PAMPAMk(representative objects)medoidmedoid(Euclidean distance)d(Oa, Ob)OaObOimedoidOjmedoidd(Oj,Oi)=min{d(Oj, Oe)} OemedoidsOjOi
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PAMmedoidOj,medoid OimedoidOhCjihCjih= d(Oj, Om) d(Oj, On) Oh Oi medoid TCih= Cjih
TCih>0OhOiOiOhTCih*
K-(PAM)kk 1kk 2k 3E 4E2 5 *
ClusterPartitional clustering algorithmPartitioning algorithmK-MeansK-Medoids*
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K-medoidsPAM CLARA (Clustering LARge Application)CLARA applies PAMO(ks^2+k(n-k))*
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CLARA -- PAM CLARA Trade-off*
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CLARA CLARANS (Clustering Large Application based upon RANdomized Search) graphO(n^2) *
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ClusterPartitional clustering algorithmPartitioning algorithmK-MeansK-Medoids*
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K meansK medoidsCLARACLARANS O(nkt)O(k(n-k)^2)O(ks^2+k(n-k))O(n^2)
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ClusterPartitional clustering algorithmPartitioning algorithmK-MeansK-Medoids*
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Supervised clustering method K supervised clustering method
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--k, http://vega.cs.tku.edu.tw/~cyh/data_mining/F7899-Ch06.pptbidm.stat.fju.edu.tw:81/STATISTICA-WEBCAST/STATISTICA-DM/DM1/K-Means%20Cluster.ppt http://mathworld.wolfram.com/K-MeansClusteringAlgorithm.html -- wolfram mathworld
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