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Content Provider | IEEE Xplore Digital Library |
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Author | Wen Xiong Cong Wang |
Copyright Year | 2011 |
Description | Author affiliation: School of Computer Science & Technology, Beijing University of Posts and Telecommunications, China (Cong Wang) || Institute of Chinese Information Processing, Beijing Normal University, China (Wen Xiong) |
Abstract | Clustering is an unsupervised machine learning method, which groups data into classes without labeled samples, and an important task in data mining. To attack the local optimum of k-means method, the paper presents a novel hybrid clustering approach, which uses adaptive ant colony optimization (ACO) to optimize the partition of data set, and utilizes enhanced particle swarm optimization (PSO) to refine the result of the adaptive ACO. Experiments displayed that the approach obtains smaller clustering evaluations on three data sets of University of California Irvine (UCI) and competitive results on two data sets of UCI, which verifying its availability. |
Starting Page | 1960 |
Ending Page | 1963 |
File Size | 228004 |
Page Count | 4 |
File Format | |
ISBN | 9781424497621 |
e-ISBN | 9781424497638 |
DOI | 10.1109/CSSS.2011.5975039 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2011-06-27 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Algorithm design and analysis Ant colony optimization Particle swarm optimization (PSO) Clustering algorithms Data mining (DM) Swarm intelligence (SI) Data mining Clustering Particle swarm optimization Ant colony optimization (ACO) Optimization Genetic algorithms |
Content Type | Text |
Resource Type | Article |
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