关键词: 交集, 投票, 聚类, 聚类组合

Abstract: Being an unsupervised learning, clustering is a division of data into groups of similar objects. This paper presents a new intersection-based clustering combination algorithm, which imitates the ways of voting. Assigns some different clustering results of a same data set, this algorithm extracts the corresponding relations of each cluster in these different clustering results first, and then compute the intersection of corresponding clusters of these results, put the remaining disputable objects to vote, finally distribute the objects in abeyance after voting to the nearest object’s cluster, or distribute the remaining disputable objects to the nearest object’s cluster without voting. The experiment indicates both methods can obviously improve the clustering performance; the result with voting is better than the result without voting.

Key words: intersection, vote, clustering, clustering combination