Research on Fuzzy C-Mean Clustering Algorithm Based on Particle Swarm Optimization and Shuffled Frog Leaping Algorithm
|School||Changsha University of Science and Technology|
|Course||Computer Software and Theory|
|Keywords||data mining clustering algorithms shuffled frog leaping algorithm particle swarm optimization algorithm fuzzy C-means clustering algorithm|
Along with the high speed development of database technology, data is inflating in an astonishing speed. And to confront such mass data and to extract effective information, data mining technology emerges.Clustering is an important part of data mining, that to divide objects of a dataset into different classes, in which, objects in the same class resemble to each other and objects belongs to different classes differ from each other. In clustering, there are lots of complex combinational optimization problems, intelligence optimization algorithms makes great contribution.This paper researches fuzzy C-mean clustering algorithm and two swarm intelligence optimization algorithms, namely the shuffled frog leaping algorithm(SFLA) and the Particle Swarm Optimization(PSO) algorithm, and the two algorithms is integrated into the former. The work mainly includes:(1)To deeply analyze the solution procedure, the parameters and the merits and demerits of SFLA. And chaos mapping system and Gaussian distribution is imported to improve the upgrade procedure aiming to the problem of easy trapped by local optimum. Simulation experiments are taken for feasibility and other analysis.(2)To deeply analyze the PSO algorithm. A new parameter, that is designed to improve the disadvantage of the C-means Clustering, integrates the PSO algorithm and SFLA into the C-means Clustering, that could convergence to global optimum against of original local optimum at an equal convergence speed. Simulation experiments are taken for validation.