Data clustering using the bees algorithm
Clustering is concerned with partitioning a data set into homogeneous groups. One of the most popular clustering methods is k-means clustering because of its simplicity and computational efficiency. K-means clustering involves search and optimization. The main problem with this clustering method is...
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| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2007
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| Subjects: | |
| Online Access: | http://repo.uum.edu.my/153/1/data_clustering.pdf http://repo.uum.edu.my/153/ |
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| Summary: | Clustering is concerned with partitioning a data set into homogeneous groups. One of the most popular clustering methods is k-means clustering because of its simplicity and computational efficiency. K-means clustering involves search and optimization. The main problem with this clustering method is its tendency to converge to local optima. The authors’ team have developed a new population based search algorithm called the Bees Algorithm that is capable of locating near optimal solutions efficiently. This paper proposes a clustering method that integrates the simplicity of the k-means algorithm with the capability of the Bees Algorithm to avoid local optima. The paper presents test results to demonstrate the efficacy of the proposed algorithm. |
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