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7.4 P.401~P.408 93156204 93156206 93156208 93156219

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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 COMPLEXITYK-means K,square error function clusters,clusters.methodO(nkt),nobject,kcluster,titeration(k

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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