Clustering Method Combining Threshold Algorithm with Ant Colony Algorithm
Zhang Zhao-tao
Abstract
Zhang Zhao-tao
Abstract
To improve the quality of clusting analysis,a novel clustering method combining the threshold algorithm with the ant colony algorithm was proposed.With this method,the center and number of clustering are determined by using the clustering algorithm based on threshold,and then the above clustering results are optimized by the K-means algorithm combining with transition probability based on the ant colony algorithm.The experimental results show that the proposed clustering method has a higher F-measure than the K-means and other algorithms.
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To improve the quality of clusting analysis,a novel clustering method combining the threshold algorithm with the ant colony algorithm was proposed.With this method,the center and number of clustering are determined by using the clustering algorithm based on threshold,and then the above clustering results are optimized by the K-means algorithm combining with transition probability based on the ant colony algorithm.The experimental results show that the proposed clustering method has a higher F-measure than the K-means and other algorithms.
Key concepts: Cluster analysis, Ant colony optimization algorithms, Algorithm, Canopy clustering algorithm, CURE data clustering algorithm, Computer science, Correlation clustering, Ant colony