NDLI: A CSA-based clustering algorithm for large data sets with mixed numeric and categorical values

Content Provider	IEEE Xplore Digital Library
Author	Li Jie Gao Xinbo Jiao Li-Cheng
Copyright Year	2004
Description	Author affiliation: Sch. of Electron. Eng., Xidian Univ., Xi'an, China (Li Jie; Gao Xinbo; Jiao Li-Cheng)
Abstract	In the field of data mining, it is often encountered to perform cluster analysis on large data sets with mixed numeric and categorical values. However, most existing clustering algorithms are only efficient for the numeric data rather than the mixed data set. For this purpose, this paper presents a novel clustering algorithm for these mixed data sets by modifying the common cost function, trace of the within cluster dispersion matrix. The clonal selection algorithm (CSA) is used to optimize the new cost function. Experimental result illustrates that the CSA-based new clustering algorithm is feasible for the large data sets with mixed numeric and categorical values.
Sponsorship	Zhejian Univ. Nat. Lab. of Ind. Control Technol. IEEE Robotics and Automation Soc. Nat. Natural Sci. Found. of China Chinese Assoc. of Automation
Starting Page	2303
Ending Page	2307
File Size	329825
Page Count	5
File Format	PDF
ISBN	0780382730
DOI	10.1109/WCICA.2004.1342001
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-06-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Cost function Data mining Data analysis Partitioning algorithms Algorithm design and analysis Data engineering Performance analysis Databases Prototypes
Content Type	Text
Resource Type	Article

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