Initializing FWSA K-Means With Feature Level Constraints
Weighted K-Means (WKM) algorithms are increasingly important with the increase of data dimension. WKM faces an initialization problem that is more complicated than K-Means’ because in addition to picking initial cluster centers, it should also provide feature weights. Moreover, the one-di...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9994728/ |
| _version_ | 1797976509304012800 |
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| author | Zhenfeng He |
| author_facet | Zhenfeng He |
| author_sort | Zhenfeng He |
| collection | DOAJ |
| description | Weighted K-Means (WKM) algorithms are increasingly important with the increase of data dimension. WKM faces an initialization problem that is more complicated than K-Means’ because in addition to picking initial cluster centers, it should also provide feature weights. Moreover, the one-dimensional solution to WKM’s widely used objective function is unacceptable in most cases. Yet, the initialization of WKM, especially the initialization of feature weight, has been largely ignored. This paper studies the problem by analyzing Feature weight self-adjustment K-Means(FWSA K-Means), a popular WKM proposed to avoid the one-dimensional solution. Experimental results suggest that the algorithm is actually easy to cluster mainly based on a single feature information when it is not well initialized. Moreover, the paper argues that initial feature weights and cluster centers are equally important in determining the final partition. Therefore it suggests using feature level constraints to improve the initialization and proposes a semi-supervised algorithm Constrained FWSA K-Means (CFWSA K-Means). The algorithm uses constraints in evaluating feature weights and clusters to guide their evolution at the stage of initialization. Experimental results suggest that it is effective and robust in utilizing constraints. In addition, if its initialization process is started by the cluster centers provided by BRIk, an initialization approach for K-Means, the performance can be further improved. |
| first_indexed | 2024-04-11T04:52:03Z |
| format | Article |
| id | doaj.art-51e465b76227487194771c210f29f3ef |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T04:52:03Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-51e465b76227487194771c210f29f3ef2022-12-27T00:00:29ZengIEEEIEEE Access2169-35362022-01-011013297613298710.1109/ACCESS.2022.32309359994728Initializing FWSA K-Means With Feature Level ConstraintsZhenfeng He0https://orcid.org/0000-0002-1714-0507College of Computer and Data Science, Fuzhou University, Fuzhou, ChinaWeighted K-Means (WKM) algorithms are increasingly important with the increase of data dimension. WKM faces an initialization problem that is more complicated than K-Means’ because in addition to picking initial cluster centers, it should also provide feature weights. Moreover, the one-dimensional solution to WKM’s widely used objective function is unacceptable in most cases. Yet, the initialization of WKM, especially the initialization of feature weight, has been largely ignored. This paper studies the problem by analyzing Feature weight self-adjustment K-Means(FWSA K-Means), a popular WKM proposed to avoid the one-dimensional solution. Experimental results suggest that the algorithm is actually easy to cluster mainly based on a single feature information when it is not well initialized. Moreover, the paper argues that initial feature weights and cluster centers are equally important in determining the final partition. Therefore it suggests using feature level constraints to improve the initialization and proposes a semi-supervised algorithm Constrained FWSA K-Means (CFWSA K-Means). The algorithm uses constraints in evaluating feature weights and clusters to guide their evolution at the stage of initialization. Experimental results suggest that it is effective and robust in utilizing constraints. In addition, if its initialization process is started by the cluster centers provided by BRIk, an initialization approach for K-Means, the performance can be further improved.https://ieeexplore.ieee.org/document/9994728/Weighted K-Meansinitializationfeature level constraintsemi-supervised clustering |
| spellingShingle | Zhenfeng He Initializing FWSA K-Means With Feature Level Constraints IEEE Access Weighted K-Means initialization feature level constraint semi-supervised clustering |
| title | Initializing FWSA K-Means With Feature Level Constraints |
| title_full | Initializing FWSA K-Means With Feature Level Constraints |
| title_fullStr | Initializing FWSA K-Means With Feature Level Constraints |
| title_full_unstemmed | Initializing FWSA K-Means With Feature Level Constraints |
| title_short | Initializing FWSA K-Means With Feature Level Constraints |
| title_sort | initializing fwsa k means with feature level constraints |
| topic | Weighted K-Means initialization feature level constraint semi-supervised clustering |
| url | https://ieeexplore.ieee.org/document/9994728/ |
| work_keys_str_mv | AT zhenfenghe initializingfwsakmeanswithfeaturelevelconstraints |