Abstract
Existing clustering ensemble selection methods adopt internal and external evaluation indexes to measure the quality and diversity of base clusterings. The significance of base clustering is quantified by the average or weighted average of multiple evaluation indexes. However, there exist two limitations in these methods. First, the evaluation of base clusterings in the form of linear combination of multiple indexes lacks the structural analysis and relative comparison between clusterings and measures. Second, the consistency between the final evaluation and the multiple evaluations from different measures cannot be guaranteed. To tackle these problems, we propose a clustering ensemble selection method with Analytic Hierarchy Process (AHPCES). Experimental results validate the effectiveness of the proposed method.
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Acknowledgment
This work was supported by National Natural Science Foundation of China (Nos. 61976134, 61991410, 61991415) and Open Project Foundation of Intelligent Information Processing Key Laboratory of Shanxi Province (No. CICIP2018001).
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Liu, W., Yue, X., Zhong, C., Zhou, J. (2020). Clustering Ensemble Selection with Analytic Hierarchy Process. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_5
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