An Enhanced Regularized k-Means Type Clustering Algorithm With Adaptive Weights
K-means clustering algorithm is one of the most popular technique for clustering in machine learning, however, in the existing k-means clustering algorithm, the ability of the different features and the importance of the different data objects are treated equally; the discriminative ability of the d...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8986606/ |
| _version_ | 1818444317328408576 |
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| author | Ziheng Wu Zixiang Wu |
| author_facet | Ziheng Wu Zixiang Wu |
| author_sort | Ziheng Wu |
| collection | DOAJ |
| description | K-means clustering algorithm is one of the most popular technique for clustering in machine learning, however, in the existing k-means clustering algorithm, the ability of the different features and the importance of the different data objects are treated equally; the discriminative ability of the different features and the importance of the different data objects cannot be differentiated effectively. In the light of this limitation, this paper put forward an enhanced regularized k-means type clustering algorithm with adaptive weights in which we introduced an adaptive feature weights matrix and an adaptive data weights vector into the objective function of the k-means clustering algorithm and we developed a new objective function with l2-norm regularization to the weights of data objects and features, then we obtained the corresponding scientific updating iterative rules of the weights of the different features, the weights of the different data objects and the cluster centers theoretically. In order to evaluate the performance of the new algorithm put forward, extensive experiments were conducted. Experimental results have indicated that our proposed algorithm can improve the clustering performance significantly and are more effective with respects to three metrics: the successful clustering rate (SCR), normal mutual information (NMI) and RandIndex. |
| first_indexed | 2024-12-14T19:14:01Z |
| format | Article |
| id | doaj.art-bf64c8ab5d3643708fa1ab6848a6c938 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T19:14:01Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bf64c8ab5d3643708fa1ab6848a6c9382022-12-21T22:50:39ZengIEEEIEEE Access2169-35362020-01-018311713117910.1109/ACCESS.2020.29723338986606An Enhanced Regularized k-Means Type Clustering Algorithm With Adaptive WeightsZiheng Wu0https://orcid.org/0000-0003-4633-6012Zixiang Wu1https://orcid.org/0000-0002-0732-9496School of Electrical and Information Engineering, Anhui University of Technology, Ma’anshan, ChinaDepartment of Thoracic Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaK-means clustering algorithm is one of the most popular technique for clustering in machine learning, however, in the existing k-means clustering algorithm, the ability of the different features and the importance of the different data objects are treated equally; the discriminative ability of the different features and the importance of the different data objects cannot be differentiated effectively. In the light of this limitation, this paper put forward an enhanced regularized k-means type clustering algorithm with adaptive weights in which we introduced an adaptive feature weights matrix and an adaptive data weights vector into the objective function of the k-means clustering algorithm and we developed a new objective function with l2-norm regularization to the weights of data objects and features, then we obtained the corresponding scientific updating iterative rules of the weights of the different features, the weights of the different data objects and the cluster centers theoretically. In order to evaluate the performance of the new algorithm put forward, extensive experiments were conducted. Experimental results have indicated that our proposed algorithm can improve the clustering performance significantly and are more effective with respects to three metrics: the successful clustering rate (SCR), normal mutual information (NMI) and RandIndex.https://ieeexplore.ieee.org/document/8986606/<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">K</italic>-means clustering algorithmmachine learningadaptive weights<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">l</italic>²-norm regularization |
| spellingShingle | Ziheng Wu Zixiang Wu An Enhanced Regularized k-Means Type Clustering Algorithm With Adaptive Weights IEEE Access <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">K</italic>-means clustering algorithm machine learning adaptive weights <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">l</italic>²-norm regularization |
| title | An Enhanced Regularized k-Means Type Clustering Algorithm With Adaptive Weights |
| title_full | An Enhanced Regularized k-Means Type Clustering Algorithm With Adaptive Weights |
| title_fullStr | An Enhanced Regularized k-Means Type Clustering Algorithm With Adaptive Weights |
| title_full_unstemmed | An Enhanced Regularized k-Means Type Clustering Algorithm With Adaptive Weights |
| title_short | An Enhanced Regularized k-Means Type Clustering Algorithm With Adaptive Weights |
| title_sort | enhanced regularized k means type clustering algorithm with adaptive weights |
| topic | <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">K</italic>-means clustering algorithm machine learning adaptive weights <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">l</italic>²-norm regularization |
| url | https://ieeexplore.ieee.org/document/8986606/ |
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