Abstract
Cluster ensemble has emerged as a powerful technique for combining multiple clustering results. However, existing cluster ensemble approaches are usually restricted to two-way clustering, and they also cannot flexibility provide two-way or three-way clustering result accordingly. The main objective of this paper is to propose a general cluster ensemble framework for both two-way decision clustering and three-way decision. A cluster is represented by three regions such as the positive region, boundary region and negative region. The three-way representation intuitively shows which objects are fringe to the cluster. In this work, the number of ensemble members is increased gradually in each decision (iteration), it is different from the existing cluster ensemble methods in which all available ensemble members join the computing at one decision. It can be ended at a three-way decision final clusters or choose to go on until all the objects are assigned to the positive or negative region of the cluster determinately. The experimental results show that the proposed gradual three-way decision cluster ensemble approach is effective for reducing the running time and not sacrificing the accuracy.
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Acknowledgment
The authors would like to thank Mr. Lingchao Hu for his help to complete the experimental work. This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61751312, 61533020 and 61379114.
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Yu, H., Wang, G. (2018). An Efficient Gradual Three-Way Decision Cluster Ensemble Approach. In: Medina, J., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations. IPMU 2018. Communications in Computer and Information Science, vol 854. Springer, Cham. https://doi.org/10.1007/978-3-319-91476-3_58
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