Algorithm for fuzzy clustering of mixed data with numeric and categorical attributes

Amir Ahmad, Lipika Dey

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

In many applications numeric as well as categorical features describe the data objects. A variety of algorithms have been proposed for clustering if fuzzy partitions and descriptive cluster prototypes are desired. However, most of these methods are designed for data sets with variables measured in the same scale type (only categorical, or only numeric). We have developed probabilistic distance measure to compute significance of attributes for numeric data, and distance between two categorical values. We used this distance measure with the cluster center definition proposed by Yasser El-Sonbaty and M. A. Ismail [26] to propose Fuzzy-c mean type clustering algorithm for mixed attributes data. The results of the application of the new algorithm show that new technique is quite encouraging.

Original languageEnglish
Title of host publicationDistributed Computing and Internet Technology - Second International Conference, ICDCIT 2005, Proceedings
Pages561-572
Number of pages12
DOIs
Publication statusPublished - 2005
Externally publishedYes
Event2nd International Conference on Distributed Computing and Internet Technology, ICDCIT 2005 - Bhubaneswar, India
Duration: Dec 22 2005Dec 24 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3816 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Conference on Distributed Computing and Internet Technology, ICDCIT 2005
Country/TerritoryIndia
CityBhubaneswar
Period12/22/0512/24/05

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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