Abstract:

Focusing on the precise definition of information measures for interval-valued fuzzy soft sets, the distance measure, the similarity measure, the entropy measure, the inclusion measure, and the subsethood measure of interval-valued fuzzy soft sets were introduced. A series of formulae of information measures were presented, and their transformation relationships were discussed. Then, combining the characteristics of interval-valued fuzzy soft sets, a clustering algorithm based on similarity measure was explored. It emphasized the clustering of similar level knowledge of experts who gave the evaluation of objects. Meanwhile, the computational complexity of the algorithm was discussed. Finally, a practical example was given to prove that the proposed algorithm can effectively handle the clustering problem of experts.

Key words: interval-valued fuzzy soft set, information measure, similarity measure, clustering algorithm, computational complexity

摘要：

针对区间值模糊软集信息测度难以精确定义的问题,提出了区间值模糊软集的距离测度、相似度、熵、包含度、子集度的公理化定义,给出了区间值模糊软集的信息测度公式,并讨论了它们的转换关系。然后提出了一个基于相似度的聚类算法,该算法结合区间值模糊软集的特性,着重对给出评价对象的具有相似知识水平的专家进行聚类,同时讨论了算法的计算复杂度。最后通过实例说明该算法能有效地处理专家聚类问题。

关键词: 区间值模糊软集, 信息测度, 相似度, 聚类算法, 计算复杂度