摘要
针对传统基于最大熵模糊C均值聚类算法(MEFCM)仅适用于球状或椭圆状聚类,为了解决数据分布混乱以及高度相关难以划分的情形,引入Mercer核函数,使原来没有显现的特征突现出来,从而使聚类效果更好.然而在实际问题中,大多数样本集的样本数据都存在着重要性(权重)不同的现象,主要针对样本集中各个数据的不同重要程度来设计加权方法,同时为了克服聚类算法对初始聚类中心选取的敏感性这一弱点,提出了一个初始聚类中心优化的加权最大熵核模糊聚类算法(WKMEFCM).通过实验验证,该算法与原MEFCM算法比较,其聚类结果更加稳定、准确,从而达到更好的聚类划分效果.
This paper aims to demonstrate the traditional maximum entropy fuzzy C-means clustering algorithm （MEFCM） applies to spherical or oval-shaped clusters only. In order to solve the confusion and highly relevant data distribution division of this difficult situation, it introduces Mercer kernel function, so that the original features which do not show can stand out and make the clustering effect better. However, in practical, the majority of sample sets are exist importance （weighting） of different phenomena. The main focus are the samples of different importance to design of each data weighting and in order to overcome the sensitivity of weakness of the initial cluster centers. This paper presents an optimization of the initial cluster centers weighted kernel maximum entropy fuzzy clustering algorithm （WKMEFCM）. Experiments show that compared with the MEFCM, the clustering result is more stable, accurate and the clusters division effect is better.
出处
《计算机系统应用》
2014年第8期139-143,共5页
Computer Systems & Applications
基金
国家自然科学基金(61100116)
江苏省自然科学基金重点资助项目(BK2011492)
关键词
核函数
FCM算法
特征权重
最大熵
初始聚类中心
kernel function
FCM algorithm
feature weighting
maximum entropy
initial cluster centers
