NDLI: Research on an Ant Colony ISODATA Algorithm for Clustering Analysis in Real Time Computer Simulation

Content Provider	IEEE Xplore Digital Library
Author	Ying Wang Ren-Wang Li Bin Li Peng-Ju Zhang Yao-Hui Li
Copyright Year	2007
Description	Author affiliation: Zhejiang Sci-Tech Univ., Hangzhou (Ying Wang; Ren-Wang Li)
Abstract	This paper intends to propose an advanced clustering method, ant colony ISODATA algorithm (ACIA) in real time computer simulation. Ant colony algorithm is used as the method of cursory clustering based on ants piling up their corpses and classifying their young ones. ISODATA algorithm is applied to meticulous clustering. This algorithm has been implemented and tested on several simulated data sets. At the same time, the performance efficiency of ACIA is analyzed based on four parameters :intracluster dissimilarity degree, intercluster dissimilarity degree, misclassification rate and CPU performance time. The computational results show that it is better than three other algorithms: ant colony K-means algorithm (ACKA), ant colony genetic algorithm (ACGA) and genetic K-means algorithm (GKA).
Starting Page	223
Ending Page	229
File Size	403897
Page Count	7
File Format	PDF
ISBN	0769530656
DOI	10.1109/DMAMH.2007.37
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-12-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Computer simulation Computational modeling Clustering algorithms Partitioning algorithms Performance analysis Application software Cadaver Testing Genetic algorithms
Content Type	Text
Resource Type	Article

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