Projected fuzzy c-means clustering algorithm with instance penalty
At present, high-dimensional data clustering has become a vital research field in machine learning. Traditional clustering algorithms cannot perform well on high-dimensional data, where the clustering task is usually divided into two stages: dimensionality reduction first and clustering later. In ge...
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| Veröffentlicht in: | Expert systems with applications Jg. 255; S. 124563 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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01.12.2024
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| ISSN: | 0957-4174 |
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| Abstract | At present, high-dimensional data clustering has become a vital research field in machine learning. Traditional clustering algorithms cannot perform well on high-dimensional data, where the clustering task is usually divided into two stages: dimensionality reduction first and clustering later. In general, the existing high-dimensional clustering methods usually have the following shortcomings: (1) the two-stage strategy splits the connection between clustering and dimensionality reduction; (2) these algorithms do not consider the impact of anomalous instances in high-dimensional data on clustering performance. Therefore, to address these problems, a projected fuzzy c-means clustering algorithm with instance penalty (PCIP) is proposed. Firstly, we construct an instance penalty matrix and assign an instance penalty coefficient to each sample. Secondly, a model for clustering high-dimensional data is constructed by integrating fuzzy c-means clustering (FCM) and principal component analysis (PCA). The proposed model can perform dimensionality reduction and clustering simultaneously. In addition, the time complexity of the proposed algorithm is linearly related to the number of samples n, which can efficiently deal with large data sets. The proposed PCIP algorithm is verified by experiments using clustering accuracy and normalized mutual information (NMI) as evaluation metrics. The experimental results on 10 image datasets show that the average accuracy and average NMI of the PCIP algorithm are improved by 0.0375 and 0.0275, respectively, compared to the second-ranked algorithm.
•The proposed PCIP accomplishes both dimensionality reduction and clustering.•The instance penalty matrix is used to identify and handle the abnormal samples.•We propose an iterative algorithm to solve PCIP and its convergence is proved.•The time complexity of PCIP is linearly related to the number of samples.•Extensive experiments have demonstrated the effectiveness of PCIP. |
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| AbstractList | At present, high-dimensional data clustering has become a vital research field in machine learning. Traditional clustering algorithms cannot perform well on high-dimensional data, where the clustering task is usually divided into two stages: dimensionality reduction first and clustering later. In general, the existing high-dimensional clustering methods usually have the following shortcomings: (1) the two-stage strategy splits the connection between clustering and dimensionality reduction; (2) these algorithms do not consider the impact of anomalous instances in high-dimensional data on clustering performance. Therefore, to address these problems, a projected fuzzy c-means clustering algorithm with instance penalty (PCIP) is proposed. Firstly, we construct an instance penalty matrix and assign an instance penalty coefficient to each sample. Secondly, a model for clustering high-dimensional data is constructed by integrating fuzzy c-means clustering (FCM) and principal component analysis (PCA). The proposed model can perform dimensionality reduction and clustering simultaneously. In addition, the time complexity of the proposed algorithm is linearly related to the number of samples n, which can efficiently deal with large data sets. The proposed PCIP algorithm is verified by experiments using clustering accuracy and normalized mutual information (NMI) as evaluation metrics. The experimental results on 10 image datasets show that the average accuracy and average NMI of the PCIP algorithm are improved by 0.0375 and 0.0275, respectively, compared to the second-ranked algorithm.
•The proposed PCIP accomplishes both dimensionality reduction and clustering.•The instance penalty matrix is used to identify and handle the abnormal samples.•We propose an iterative algorithm to solve PCIP and its convergence is proved.•The time complexity of PCIP is linearly related to the number of samples.•Extensive experiments have demonstrated the effectiveness of PCIP. |
| ArticleNumber | 124563 |
| Author | Huang, Xueyan Zhang, Cuihong Wu, Yiwen Wang, Jikui Nie, Feiping |
| Author_xml | – sequence: 1 givenname: Jikui orcidid: 0000-0001-5926-7007 surname: Wang fullname: Wang, Jikui email: wjkweb@163.com organization: School of Information Engineering and Artificial Intelligence, Lanzhou University of Finance and Economics, Lanzhou 730000, Gansu, China – sequence: 2 givenname: Yiwen surname: Wu fullname: Wu, Yiwen email: 2516482760@qq.com organization: School of Economics and Management, Dalian University of Technology, Dalian 116024, China – sequence: 3 givenname: Xueyan surname: Huang fullname: Huang, Xueyan email: 838815750@qq.com organization: School of Information Engineering and Artificial Intelligence, Lanzhou University of Finance and Economics, Lanzhou 730000, Gansu, China – sequence: 4 givenname: Cuihong surname: Zhang fullname: Zhang, Cuihong email: 1942819811@qq.com organization: School of Information Engineering and Artificial Intelligence, Lanzhou University of Finance and Economics, Lanzhou 730000, Gansu, China – sequence: 5 givenname: Feiping surname: Nie fullname: Nie, Feiping email: feipingnie@gmail.com organization: School of Artificial Intelligence, Optics and ElectroNics(iOPEN), Northwestern Polytechnical University, Xi’an 710072, Shaanxi, China |
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| Keywords | Dimensionality reduction Fuzzy c-means clustering Instance penalty Principal component analysis |
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