关键词: 区间型数据, 模糊c均值聚类, 自适应系数, 自适应模糊c均值聚类

Abstract: A self-adapting fuzzy c means clustering algorithm is proposed for clustering problems with interval data. On the one hand, based on the mid-point and half-width of interval value, the impact factor of interval width is introduced to control the influence on the clustering results by the length of the interval data. On the other hand, the impact on the clustering results by the data structure of the interval data can also be reduced by introducing a self-adapting coefficient. The validity of the algorithm can be demonstrated by synthetic data and Fish real data, and the clustering results are compared and analyzed.

Key words: interval data, fuzzy c means clustering, self-adapting coefficient, self-adapting fuzzy c means clustering