JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
Incremental Fuzzy Clustering Based on a Fuzzy Scatter Matrix |
| 영문제목(English Title) |
Incremental Fuzzy Clustering Based on a Fuzzy Scatter Matrix |
| 저자(Author) |
Yongli Liu
Hengda Wang
Tianyi Duan
Jingli Chen
Hao Chao
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| 원문수록처(Citation) |
VOL 15 NO. 02 PP. 0359 ~ 0373 (2019. 04) |
한글내용
(Korean Abstract) |
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영문내용
(English 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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| 키워드(Keyword) |
Fuzzy Clustering
Incremental Clustering
Scatter Matrix
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| 파일첨부 |
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