Incremental fuzzy clustering based on a fuzzy scatter matrix

Yongli Liu, Hengda Wang, Tianyi Duan, Jingli Chen and Hao Chao
Volume: 15, No: 2, Page: 359 ~ 373, Year: 2019
10.3745/JIPS.01.0040
Keywords: Fuzzy Clustering, Incremental Clustering, Scatter Matrix
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Abstract
For clustering large-scale data, which cannot be loaded into memory entirely, incremental clustering algorithms are very popular. Usually, these algorithms only concern the within-cluster compactness and ignore the between-cluster separation. In this paper, we propose two incremental fuzzy compactness and separation (FCS) clustering algorithms, Single-Pass FCS (SPFCS) and Online FCS (OFCS), based on a fuzzy scatter matrix. Firstly, we introduce two incremental clustering methods called single-pass and online fuzzy C-means algorithms. Then, we combine these two methods separately with the weighted fuzzy C-means algorithm, so that they can be applied to the FCS algorithm. Afterwards, we optimize the within-cluster matrix and betweencluster matrix simultaneously to obtain the minimum within-cluster distance and maximum between-cluster distance. Finally, large-scale datasets can be well clustered within limited memory. We implemented experiments on some artificial datasets and real datasets separately. And experimental results show that, compared with SPFCM and OFCM, our SPFCS and OFCS are more robust to the value of fuzzy index m and noise.

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Cite this article
IEEE Style
Y. Liu, H. Wang, T. Duan, J. Chen and H. Chao, "Incremental fuzzy clustering based on a fuzzy scatter matrix," Journal of Information Processing Systems, vol. 15, no. 2, pp. 359~373, 2019. DOI: 10.3745/JIPS.01.0040.

ACM Style
Yongli Liu, Hengda Wang, Tianyi Duan, Jingli Chen, and Hao Chao. 2019. Incremental fuzzy clustering based on a fuzzy scatter matrix, Journal of Information Processing Systems, 15, 2, (2019), 359~373. DOI: 10.3745/JIPS.01.0040.