NDLI: GAKC: A New GA-Based k Clustering Algorithm

Content Provider	IEEE Xplore Digital Library
Author	Li Xiaohong Luo Min
Copyright Year	2009
Abstract	Clustering is an important, hard and active topic in data analysis and pattern recognition. K clustering is a branch of data clustering where the number of clusters is know in advance. Recently, spectral clustering (SC) becomes one of the most popular and appealing k clustering methods because of its generality, efficiency and its rich theoretical foundation. But the final results obtained from SCs depend on spectral relaxation which may have no guarantee on the quality of the solution. In order to overcome the SCs’ shortcoming, we propose an effective GAKC algorithm by using a genetic algorithm to search for the optimal cluster result of SCs. The algorithm uses group number coding chromosome, a new uniform crossover operator and exponential mutation rate. To verify the effectiveness of GAKC, a comparison among the experimental results of the proposed GAKC, a classical GA-based method by Ujjwal Malulik and the SC methods by SM and NJW on a real-life data set is presented. The conclusion comes that the proposed algorithm can gain much more accurate clustering result.
Starting Page	334
Ending Page	338
File Size	281344
Page Count	5
File Format	PDF
ISBN	9781424463251
e-ISBN	9781424463268
DOI	10.1109/ISISE.2009.115
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data analysis Clustering methods Laboratories Data engineering pattern recongnition Pattern recognition Partitioning algorithms Genetic algorithms genetic algorithm Information science Clustering algorithms Information security Data clustering spectral clustering
Content Type	Text
Resource Type	Article

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