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Incremental Fuzzy Clustering Based on a Fuzzy Scatter Matrix
Liu, Yongli,Wang, Hengda,Duan, Tianyi,Chen, Jingli,Chao, Hao Korea Information Processing Society 2019 Journal of information processing systems Vol.15 No.2
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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Incremental fuzzy clustering based on a fuzzy scatter matrix
Yongli Liu,Hengda Wang,Tianyi Duan,Jingli Chen,Hao Chao 한국정보처리학회 2019 Journal of information processing systems Vol.15 No.2
For clustering large-scale data, which cannot be loaded into memory entirely, incremental clustering algorithmsare very popular. Usually, these algorithms only concern the within-cluster compactness and ignore thebetween-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-meansalgorithms. Then, we combine these two methods separately with the weighted fuzzy C-means algorithm, sothat they can be applied to the FCS algorithm. Afterwards, we optimize the within-cluster matrix and betweenclustermatrix simultaneously to obtain the minimum within-cluster distance and maximum between-clusterdistance. Finally, large-scale datasets can be well clustered within limited memory. We implemented experimentson some artificial datasets and real datasets separately. And experimental results show that, compared withSPFCM and OFCM, our SPFCS and OFCS are more robust to the value of fuzzy index m and noise.
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