Fuzzy ant based spatial clustering

Various clustering methods based on the behavior of real ants have been proposed. In this paper, we develop a new algorithm in which the behavior of the artificial ants is governed by fuzzy set. Firstly, we define the average distance between objects, and the average distance is the domain of the object similarity. Secondly, the similarity between objects is mapped a domain of fuzzy sets by membership function. Finally, by the given confidence level, fuzzy sets will be separated into universal set. The universal set will decide that ants pick up or put down the object. In the experiment, spatial data source comes from the actual survey data in mine. LF algorithm and the fuzzy ant based spatial clustering algorithm separately to cluster these data. Through analysis and comparison the experimental results to prove that the fuzzy ant based spatial clustering algorithm enhances the clustering effect.