2012Computer Engineering and Applications JournalOpen access

K-medoids clustering algorithm method based on ant colony algorithm

Lin Wang

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Abstract

K-medoids algorithm as a kind of clustering algorithm, not easily affected by extreme data, the influence of broad adaptability, but K-medoids clustering algorithm accuracy is not stable, average accuracy, low in the real,clustering analysis effect is poorer. ACO is a bionic optimization algorithm, which has strong robustness, can be unified easily with other method, has high efficiency. K-medoids clustering algorithm based on ACO algorithm merit reference, this paper proposes a new clustering algorithm. It raises the clustering algorithm, and the stability of the accuracy is high. Finally, simulation experiments show the feasibility and advantage of this algorithm.

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What this paper is about

K-medoids algorithm as a kind of clustering algorithm, not easily affected by extreme data, the influence of broad adaptability, but K-medoids clustering algorithm accuracy is not stable, average accuracy, low in the real,clustering analysis effect is poorer. ACO is a bionic optimization algorithm, which has strong robustness, can be unified easily with other method, has high efficiency. K-medoids clustering algorithm based on ACO algorithm merit reference, this paper proposes a new clustering algorithm. It raises the clustering algorithm, and the stability of the accuracy is high. Finally, simulation experiments show the feasibility and advantage of this algorithm.

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Available abstract

K-medoids algorithm as a kind of clustering algorithm, not easily affected by extreme data, the influence of broad adaptability, but K-medoids clustering algorithm accuracy is not stable, average accuracy, low in the real,clustering analysis effect is poorer. ACO is a bionic optimization algorithm, which has strong robustness, can be unified easily with other method, has high efficiency. K-medoids clustering algorithm based on ACO algorithm merit reference, this paper proposes a new clustering algorithm. It raises the clustering algorithm, and the stability of the accuracy is high. Finally, simulation experiments show the feasibility and advantage of this algorithm.

Key concepts: k-medoids, Cluster analysis, Algorithm, Canopy clustering algorithm, CURE data clustering algorithm, Computer science, Correlation clustering, Adaptability

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