Clustering ensembles based on normalized edges

Authors:
Yan Li;Jian Yu;Pengwei Hao;Zhulin Li
Affiliations:
Center for Information Science, Peking University, Beijing, China;Inst. of Computer Science & Engineering, Beijing Jiaotong Univ., Beijing, China;Center for Information Science, Peking University, Beijing, China and Dept. of Computer Science, Queen Mary, Univ. of London, London, UK;Center for Information Science, Peking University, Beijing, China
Venue:
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2007

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Abstract

The co-association (CA) matrix was previously introduced to combine multiple partitions. In this paper, we analyze the CA matrix, and address its difference from the similarity matrix using Euclidean distance. We also explore how to find a proper and better algorithm to obtain the final partition using the CA matrix. To get more robust and reasonable clustering ensemble results, a new hierarchical clustering algorithm is proposed by developing a novel concept of normalized edges to measure the similarity between clusters. The experimental results of the proposed approach are compared with those of some single runs of well-known clustering algorithms and other ensemble methods and the comparison clearly demonstrates the effectiveness of our algorithm.