基于模糊集的蚁群聚类算法的改进

Improvement of fuzzy-set-based ant colony clustering algorithm

改进了LF算法,提出了一种基于模糊集理论的蚁群聚类新方法。首先定义了平均距离,其次在“相似”的概念上引入模糊集理论,定义了数据对象与其邻域内对象相似程度的隶属函数,最后该数据对象的拾起或放下由隶属度与置信水平λ相比较来决定。该算法避免了LF算法中不相似的数据对象本该被拾起而可能未被拾起,相似的数据对象本该被放下而可能未被放下的弊端,并简化了LF算法。

LF algorithm was improved and a new method of ant colony clustering based on fuzzy set theory was put forward. Firstly, the average distance was defined, Then, fuzzy set theory was introduced into the concept of similarity, and the membership function of similar degree between a data object and its neighbor was defined. Finally, the pickup or drop of this data object was determined by the comparison between degree of membership and confidence level λ. The new method overcomes such shortcomings in LF algorithm as that dissimilar data object may not be picked up and similar data object may not be dropped, and simplifies LF algorithm.