Abstract
In this paper, we propose a three-way decision clustering approach for high-dimensional data. First, we propose a three-way K-medoids clustering algorithm, which produces clusters represented by three regions. Objects in the positive region of a cluster certainly belong to the cluster, objects in the negative region of a cluster definitively do not belong to the cluster, and objects in the boundary region of a cluster may belong to multiple clusters. Then, we propose the novel three-way decision clustering approach using random projection method. The basic idea is to apply the three-way K-medoids several times, increasing the dimensionality of the data after each iteration of three-way K-medoids. Because the center of the project result is used to be the initial center of the next projection, the time of computing is greatly reduced. Experimental results show that the proposed clustering algorithm is suitable for high-dimensional data and has a higher accuracy and does not sacrifice the computing time.
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This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61379114 & 61533020.
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Yu, H., Zhang, H. (2016). A Three-Way Decision Clustering Approach for High Dimensional Data. In: Flores, V., et al. Rough Sets. IJCRS 2016. Lecture Notes in Computer Science(), vol 9920. Springer, Cham. https://doi.org/10.1007/978-3-319-47160-0_21
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DOI: https://doi.org/10.1007/978-3-319-47160-0_21
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