Incremental fuzzy clustering based on a fuzzy scatter matrix
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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.
Abstract
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.
참고문헌 (21)
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