摘要： 针对IADFCM算法在运算过程中忽略区间中点和半宽对区间数分析的问题，给出基于中点、半宽含权重区间数间的欧氏距离，提出一种改进的聚类分析算法，对模拟数据集和实际数据集分别进行仿真实验，实验结果表明，该算法是有效的。

关键词: 模糊C均值算法, IADFCM算法, 区间属性数据, 含噪数据

Abstract: Aiming at the problems that it ignores the different effect which the median and wide of interval play on the analysis of interval number, this paper presents a new measure of distance based on the median and wide of interval with different weight. On basis of this, a revised clustering algorithm is proposed. The experiments of the simulative and practical datasets are made by this algorithm and IADFCM. Experimental results indicate this algorithm is effective.

Key words: Fuzzy C-Means(FCM) algorithm, IADFCM algorithm, interval attribute data, noisy data

中图分类号: 

• TP311.52