Document Details

Document Type : Article In Journal 
Document Title :
A k-means type clustering algorithm for subspace clustering of mixed numeric and categorical datasets
خوارزمية التجميع k-means لتجميع فضاء جزئي من مجموعات البيانات الرقمية
 
Subject : Computer Science 
Document Language : English 
Abstract : Almost all subspace clustering algorithms proposed so far are designed for numeric datasets. In this paper, we present a k-means type clustering algorithm that finds clusters in data subspaces in mixed numeric and categorical datasets. In this method, we compute attributes contribution to different clusters. We propose a new cost function for a k-means type algorithm. One of the advantages of this algorithm is its complexity which is linear with respect to the number of the data points. This algorithm is also useful in describing the cluster formation in terms of attributes contribution to different clusters. The algorithm is tested on various synthetic and real datasets to show its effectiveness. The clustering results are explained by using attributes weights in the clusters. The clustering results are also compared with published results. 
ISSN : 0167-8655 
Journal Name : Pattern Recognition Letters 
Volume : 32 
Issue Number : 7 
Publishing Year : 1432 AH
2011 AD 
Article Type : Article 
Added Date : Wednesday, November 6, 2013 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
أمير احمدAhmad, Amir ResearcherDoctorateamirahmad01@gmail.com

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