Detail Introduction to Five Data Clustering Algorithms   Oleh:  Moertini, Veronica S. Jenis: Article from Bulletin/Magazine Dalam koleksi: Integral: Majalah Ilmiah Matematika dan Ilmu Pengetahuan Alam vol. 7 no. 2 (Oct. 2002), page 87-96. Topik: algorithms; clustering algorithms Ketersediaan Perpustakaan Pusat (Semanggi) Nomor Panggil: II48.1 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada     Lihat Detail Induk Isi artikel Clustering is one of the primary tasks of data mining. Over the years, many methods have been developed for clustering patterns. Each method can have its own technique (i. e. partitioning or hierarchical), mode (on - line or off - line), approach (fuzzy or crisp clustering), or special purpose (i. e. for sequential data set, very large database, etc). This paper aims to introduce the most representative algorithms used in off line mode that apply crisp or fuzzy approach. The algorithms are K - means, fuzzy C - means (FCM), mountain, subtractive and psFCM. The implementation of two of the algorithms using matlab is provided in the appendix. Opini Anda Klik untuk menuliskan opini Anda tentang koleksi ini! Kembali

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