NDLI: An improved ant swarm algorithm for clustering analysis of data mining

Content Provider	IEEE Xplore Digital Library
Author	Qingping Fu
Copyright Year	2009
Description	Author affiliation: School of Computer and Information Engineering. Jiangxi Normal University, Nanchang, China (Qingping Fu)
Abstract	Data Mining Technology from the huge amount of data found in the potential of useful information and knowledge, Ant colony algorithm in dealing with combinatorial optimization problem has also been made a better results, this makes the ant colony algorithm applied to data mining prospects. In order to overcome the defects of K-means algorithm such as the local optima and sensitivity to initialization and noise data, a new Ant-Cluster algorithm is put forward in this paper. The algorithm uses the capacity of global search in ant algorithm, and solves the problems of K-means. The experiment shows that the algorithm is correct, efficient and fast, and increases the convergence speed.
Starting Page	347
Ending Page	350
File Size	1019879
Page Count	4
File Format	PDF
ISBN	9781424446995
DOI	10.1109/ICTM.2009.5413035
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-05
Publisher Place	Hong Kong
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Ant colony optimization Data analysis Clustering methods Clustering algorithms Partitioning algorithms Data mining Particle swarm optimization Clustering algorithm Cadaver Testing Ant algorithm
Content Type	Text
Resource Type	Article

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