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Determining the best K for clustering transactional datasets – A coverage density-based approach

Determining the best K for clustering transactional datasets – A coverage density-based approach. Presenter : Lin, Shu -Han Authors : Hua Yan, Keke Chen, Ling Liu, Joonsoo Bae. Data & Knowledge Engineering (DKE) 68 (2009) 28–48. Outline. Motivation Objective Methodology

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Determining the best K for clustering transactional datasets – A coverage density-based approach

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  1. Determining the best K for clustering transactional datasets–A coverage density-based approach Presenter : Lin, Shu-Han Authors : Hua Yan, Keke Chen, Ling Liu, JoonsooBae Data & Knowledge Engineering(DKE) 68 (2009) 28–48

  2. Outline • Motivation • Objective • Methodology • Experiments • Conclusion • Comments

  3. Motivation Booleanvalues Clusterthetransactional datasets– akindofspecialcategoricaldata Timecomplexity:O(dmN2logN) 3

  4. Objectives • TodesignamethodACTD(AgglomerativeClusteringalgorithmwithTransactional-cluster-modesDissimilarity)especiallyfortransactionaldata • Instead of ACE (Agglomerative Categorical clustering with Entropy criterion) • Findbest-K • Moreefficiently 4

  5. Methodology–OverviewofSCALE (Sampling,ClusteringstructureAssessment,cLustering&domain-specficEvaluation) Agglomerative ACE ACTD BKPlot DMDI 5

  6. Methodology– ACTDIntra-cluster similarity Nk Mk in this case, only c is the transactional-cluster-mode • Coverage Density • Transactional-cluster-mode • A subset of items 6

  7. Methodology– ACTDInter-cluster similarity • [0, .5] Transactional-cluster-mode dissimilarity Timecomplexity:O(dmN2logN) O(MN2logN) 7

  8. Methodology– DMDI Valleys、 changedramatically 8

  9. Experiments – Performance 9

  10. Experiments – Quality 10

  11. Experiments – Quality on sample dataset Withnoise 11

  12. Conclusions TheACTD • TheCoverageDensity-basedmethodispromisingfortransactionaldatasets • Faster • Morestable thanentropy-basedmethod • TheAgglomerativeHierarchicalclusteringalgorithmandDMDIcanhelptofindbest-K

  13. Comments • Advantage • … • Drawback • … • Application • …

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