数据挖掘中模糊C聚类算法的寻优能力优化  

Optimization of the Fuzzy C Clustering Algorithm in Data Mining

作　　者：陈佳[1] 石林[2] 

机构地区：[1]江苏省联合职业技术学院刘国钧分院,江苏常州2130256 [2]常州大学,江苏常州213164

出　　处：《科技通报》2015年第9期208-211,共4页Bulletin of Science and Technology

摘　　要：随着数据库技术的不断发展,现有的聚类分析算法已经无法满足数据挖掘的聚类分析的需求。本文主要针对传统的模糊C聚类算法,将其中存在的问题进行改进,并在核函数和权值优化的基础上,提出模糊C聚类算法。首先对模糊C聚类算法的特征权值进行优化,然后引入核函数的概念,对模糊C聚类算法的寻优过程进行改进。仿真验证得到的结果是,本文所提出的模糊C聚类算法,能够在核函数和权值优化上有更好和更加稳定的聚类。With the continuous development of database technology, clustering analysis algorithms have been unable to meet the needs of the clustering analysis of data mining. According to the conventional fuzzy C clustering algorithm for mining clustering problems in data, and proposes a fuzzy C clustering algorithm based on kernel function and optimization of the weights, the feature weight fuzzy C clustering algorithm is optimized, then introduces the concept of kernel function, the fuzzy C clustering is improved algorithm. The simulation results show that, the clustering analysis of kernel function and optimization of the weights of fuzzy C clustering algorithm based on data mining in the proposed optimization ability is stronger, better.

关 键 词：数据挖掘 模糊C聚类算法 特征权值优化 核函数优化 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]