改进的FCM聚类算法在Weka平台的应用

模糊C-均值聚类算法是目前应用最广泛的聚类算法,但其仍然存在对孤立点敏感及对初始中心点依赖等问题.为此,提出了一种改进的基于样本加权的模糊聚类算法,该算法可以更加准确的获得初始中心点且去除噪声点.同时,针对Weka系统中聚类算法的薄弱性以及聚类问题在数据挖掘领域的广泛性,本文对此平台进行二次开发并对传统FCM算法与改进算法进行研究.研究发现,改进算法使得聚类结果稳定,且能准确获得聚类结果,提高了算法准确率.

Application of an Improved FCM Clustering Algorithm on Weka Platform

The fuzzy C-means clustering algorithm is the most widely used clustering algorithm, but it still remains sensitive to outliers and dependent on initial centers and other issues. Therefore, this paper presents an improving fuzzy clustering algorithm based on sample weighting, the algorithm can get more accurate initial center points and remove noise. At the same time, to the weakness of the clustering algorithm in Weka system and the clustering problem is extensive in the field of data mining, this paper makes the platform the secondary development, researches the traditional FCM algorithm and improving algorithm. The study finds that the improving algorithm makes the clustering results stable, obtain the accurate clustering results and improve the accuracy of the algorithm.