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K-MEANS, K-MEDOIDS VE BULANIK C-MEANS ALGORİTMALARININ UYGULAMALI OLARAK PERFORMANSLARININ TESPİTİ

APPLIED PERFORMANCE DETERMINATION OF K-MEANS, K-MEDOIDS AND FUZZY C-MEANS ALGORITHMS

Journal Name:

  • İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi

Publication Year:

  • 2007

Key Words:

Keywords (Original Language):

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
Partition based clustering algorithms divideobjects to the clusters according to the given input parameter. Partition based clustering algorithms are succesful to find center based clusters. In this study, partition based clustering algorithms such as k-means, k-medoids and fuzzy c-means algorithms are compared according to their clustering abilities and performances. Syntetic data sets existingin literature are used in the experiments.
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Abstract (Original Language): 
Kümeleme algoritmalarından bölünmeli kümeleme tekniği, nesneleri giriş parametre sayısıkadar kümeye bölmektedir. Bölünmeli kümeleme algoritmaları, merkez tabanlı kümeleri tespit etmede başarılıdır. Bu çalışmada, başlıca bölünmeli kümeleme algoritmalarından k-means, k-medoids ve bulanık c-means algoritmalarının kümeleme yetenekleri ve performansları karşılaştırılmıştır. Literatürde yer alan sentetik veri setleri kullanılmıştır.
FULL TEXT (PDF): 
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  • 11
31-45
  • Turkish

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