Rough Set and K-Means Clustering Algorithm Based on Ant Colony Algorithm

Abstract:

Traditional K-means clustering methods have great attachment to the selection of the initial value and easily get into the local extreme value. This paper proposes a synthetic clustering algorithm of rough set and K-means based on Ant colony algorithm. While the rough set theory presents processing method of uncertain boundary objects, Ant colony algorithm is a bionic optimization algorithm, which has strong robustness, easily with other method unifies, solving efficiency higher characteristic.. Therefore, the K-means algorithm based on Ant colony algorithm in this paper combines rough set theory with simulated annealing algorithm and K-means, in which K means cluster number and initial cluster centers can be obtained dynamically with the principle of maximum minimum, and processing boundary objects with upper and lower approximation of rough set theory. Finally, the UCIs Iris set is used to test the algorithm. The experimental results show that the algorithm has higher accuracy rate, faster execution time and more stable performance.

• Periodical: Applied Mechanics and Materials (Volume 392)
• Pages: 837-840
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.392.837
• Online since: September 2013
• Authors: Ying Meng, Ke Luo, Jian Hua Liu
• Keywords: Ant Colony Algorithm (ACA), Clustering Analysis, K-Means Algorithm, Rough Set (RS)

References

[1] Zhang Yong, Wu Wenjian, Liu Zhiming．Camouflage color selection based on improved K-means clustering [J]. Computer Engineering and Application, 2009, 45(6): 210-212.

[2] Zhao Baojiang, Li Shiyong. Nonlinear-systems identification based on ant colony clustering algorithm [J], Control and Decision, 2007, 22 (10): 1193–1196.

[3] Wang biao, Duan Shanlun, Wu Hao, etal. Fuzzy set and rough set research and application [M], Beijing: Publishing House of Electronics Industry, 2008: 1-8.

[4] Liu Ting, Guo haixiang, Zhu Kejun, etal. An Improved Genetic k-means clustering algorithm [J]. Mathematics in Practice and Theory, 2007, 37(8): 105-108.

[5] Sun Xiuguan, Liu Xiyu. New genetic K-means clustering algorithm based on meliorated initial center [J]. Computer Engineering and Application, 2008, 44(23): 166-168.

[6] Hong Liangliang, Luo Ke. Improved rough clustering method based on genetic algorithm [J]. Computer Engineering and Application, 2010, 46(25): 142-145.

[7] Liu Sige, Cheng Haozhong, Cui Wenjia. Optimal Model of Multi-objective Electric Power Network Planning Based on Rough Set Theory [J]. Proceedings of the CSEE, 2007, 27(7): 65-69.

[8] Information on http：/www. ics. uci. edu/~mlearn/MLRepository. html.

[9] Bai Xue-feng, Jiang Guo-dong. Load modeling based on improved K-means clustering algorithm and its application [J]. Electric Power Automation Equipment, 2010, 30(7)：80-83.

[10] Xing Ji-peng, Zou Xue-cheng, Liu Zheng-lin, etal. Hardware/Software Partitioning Based on K-means Clustering and simulated Annealing [J]. Computer Engineering and Application, 2006, 16(61): 431-471.