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Pertemuan XIII

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  1. Pertemuan XIII FUNGSI MAYOR Clustering

  2. Definition • Clustering is “the process of organizing objects into groups whose members are similar in some way”. • A cluster is therefore a collection of objects which are “similar” between them and are “dissimilar” to the objects belonging to other clusters.

  3. Pengklusteranmerupakanpengelompokan record, pengamatan, ataumemperhatikandanmembentukkelasobjek-objek yang memilikikemiripan. • Beberapaalgoritmapengelompokkandiantaranyaadalah EM dan Fuzzy C-Means

  4. Clustering Main Features • Clustering – a data mining technique • Usage: • Statistical Data Analysis • Machine Learning • Data Mining • Pattern Recognition • Image Analysis • Bioinformatics

  5. How many clusters? Six Clusters Two Clusters Four Clusters Notion of a Cluster can be Ambiguous

  6. Distance based method • In this case we easily identify the 4 clusters into which the data can be divided; the similarity criterion is distance: two or more objects belong to the same cluster if they are “close” according to a given distance. This is called distance-based clustering.

  7. Limitations of K-means: Non-globular Shapes Original Points K-means (2 Clusters)

  8. Limitations of K-means: Differing Sizes K-means (3 Clusters) Original Points

  9. Types of Clustering • Hierarchical • Finding new clusters using previously found ones • Partitional • Finding all clusters at once

  10. A Partitional Clustering Partitional Clustering Original Points

  11. Hierarchical Clustering Traditional Hierarchical Clustering Traditional Dendrogram Non-traditional Hierarchical Clustering Non-traditional Dendrogram

  12. AlgoritmaPengelompokan K-Means Langkah-langkahalgoritma K-Means: • Tentukanberapakelompok yang akandibuatsebanyak k kelompok. • Secarasembarangpilih k buahcatatan yang adasebagaipusat-pusatkeompokawal. • Setiapcatatanakanditentukanpusatkelompokterdekatnya. • Perbaruipusat-pusatkelompok. • Pusatkelompok yang terdekatpadasetiapcatatanakanditentukan, danseterusnyasampainilairasiotidakmembesarlagi.

  13. RumusJarakduatitik: Between Cluster Variation (BCV): BCV=d(m1,m2)+d(m1,3)+d(m2,m3) Dalamhalini, d(mi,j) menyatakanjarak mikemj Within Cluster Variation (WCV): WCV=(jarakpusattiap cluster yang paling minimum)2