Perbandingan Metode Model-Based Dengan Metode K-Mean Dalam Analisis Cluster  

Oleh:

Pardede, Timbul

Jenis: Article from Journal - ilmiah nasional
Dalam koleksi: Jurnal Matematika, Sains, dan Teknologi vol. 8 no. 2 (Sep. 2007), page 98-108.
Topik: BIC; EM Algorithm; K-Mean Clustering; Model-Based Clustering Method
Fulltext: 03timbul.pdf (222.06KB)

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: JJ138.1 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

K-mean method is a clustering method in which grouping techniques are based only on distance measure among observed objects, without considering statistical aspects. Model-based clustering is a method that use statistical aspects, as its theoretical basi i.e. probability maximum criterion. This model has several variations with a variety of geometrical characteristics obtained by mean Gauss component. Data partition is conducted by utilizing EM (expectation-maximization) algorithm. Then by using Bayesian Information Criterion (BIC) the best model is obtained. This research aimed to comparing result of grouping methods between model-based clustering and K-mean clustering. The results showed that model-based-clustering was more effective in separating overlap groups than K-mean.

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