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融合粒子群和蛙跳算法的模糊C-均值聚类算法研究
Research on Fuzzy C-Mean Clustering Algorithm Based on Particle Swarm Optimization and Shuffled Frog Leaping Algorithm
【作者】 李真；
【导师】 罗可；
【作者基本信息】 长沙理工大学 ， 计算机软件与理论， 2011， 硕士
【摘要】 随着数据库技术的飞速发展,数据以惊人的速度膨胀,面多如此海量的数据,为了从中提取有效的信息,数据挖掘技术孕育而生。聚类分析是数据挖掘的一个重要部分,聚类分析就是把数据对象集合中的不同数据对象划分成不同的类的过程,同一个类里的数据对象彼此相似,不同的类之间数据对象彼此相异。聚类分析中的有许多复杂组合的优化问题,智能优化算法能为之提供有效的帮助。本文深入研究了群智能算法中混合蛙跳算法和粒子群算法与聚类分析中模糊C-均值聚类算法,并将三者融合在一起。主要工作包括:(1)深入分析了混合蛙跳算法的求解过程、参数设定和优缺点,针对算法易陷入局部最优的问题,引进了混沌映射系统和高斯分布改进了算法中的更新步骤。通过实验仿真,验证算法的可行性,并做了相关分析。(2)研究了粒子群算法的特点,为克服模糊C-均值聚类算法的不足,通过设计一个参数,有机地将改进的混合蛙跳算法和粒子群算法融合到模糊C-均值聚类算法中。使算法能够较好地跳出局部最优解,收敛于全局最优,同时也保证了收敛速度。最后通过实验仿真,验证了算法的有效性。
【Abstract】 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.