A Novel Fuzzy Clustering Algorithm Based on Similarity of Attribute Space

Weifeng SHI; Jinbao ZHUO; Ying LAN

doi:10.11999/JEIT180974

Volume 41 Issue 11

Nov. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(11): 2722-2728

Weifeng SHI, Jinbao ZHUO, Ying LAN. A Novel Fuzzy Clustering Algorithm Based on Similarity of Attribute Space[J]. Journal of Electronics & Information Technology, 2019, 41(11): 2722-2728. doi: 10.11999/JEIT180974

Citation:

Weifeng SHI, Jinbao ZHUO, Ying LAN. A Novel Fuzzy Clustering Algorithm Based on Similarity of Attribute Space[J]. Journal of Electronics & Information Technology, 2019, 41(11): 2722-2728. doi: 10.11999/JEIT180974

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A Novel Fuzzy Clustering Algorithm Based on Similarity of Attribute Space

doi: 10.11999/JEIT180974

Shanghai Maritime University, Shanghai 201306, China

Funds: The National Natural Science Foundation of China (61503240), Shanghai Maritime University Graduate Student Innovation Fund Project (2016ycx078)

Received Date: 2018-10-17
Rev Recd Date: 2019-02-28

Available Online: 2019-04-25

Publish Date: 2019-11-01

Abstract

Abstract

With the attribute feature information of the fuzzy membership matrix and cluster centers after the iteration not fully utilized, the results of Fuzzy C-Means (FCM) Clustering and related modified algorithms are determined based on the principle of maximum fuzzy membership, causing bad influence on the clustering accuracy. To solve this problem, the improvement ideas are proposed: to improve classification principle of FCM. The formula definition of attribute similarity in binary topological subspaces is given. Then, the improved FCM algorithm based on the Similarity of Attribute Space (FCM-SAS) is proposed: First, samples with fuzzy membership degree lower than the clustering reliability are selected as suspicious samples. Next, the attribute similarity between the suspicious samples and the cluster centers after clustering are calculated. Finally, cluster labels of suspicious samples based on the principle of maximum attribute similarity are updated. The validity and superiority of the proposed algorithm is verified by the UCI sample set experiments and comparisons with other modified algorithms based on the principle of maximum fuzzy membership.
- Fuzzy C-Means (FCM) clustering,
- Attribute topology subspace,
- Attribute similarity,
- Clustering reliability,
- Principle of maximum attribute similarity

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References(26)

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