Two-Level-Oriented Selective Clustering Ensemble Based on Hybrid Multi-Modal Metrics
The purpose of selective clustering ensemble is to select a subset of base clustering partitions with predictive performance and combine these partitions into more accurate and stable final results. Traditional approaches tend to utilize the well-known validity criteria such as NMI to evaluate the q...
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8506340/ |
| _version_ | 1818618855796244480 |
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| author | Hongling Wang Gang Liu |
| author_facet | Hongling Wang Gang Liu |
| author_sort | Hongling Wang |
| collection | DOAJ |
| description | The purpose of selective clustering ensemble is to select a subset of base clustering partitions with predictive performance and combine these partitions into more accurate and stable final results. Traditional approaches tend to utilize the well-known validity criteria such as NMI to evaluate the quality and diversity of base clustering partitions in the selection process. However, the characteristics of the original data and the data structure itself are commonly neglected. Furthermore, the generation process of base clustering partitions is more concerned with diversity and less consideration of quality. To tackle these problems, we propose a new selective clustering ensemble scheme. In the process of generating base clustering partitions, k-means and hierarchical clustering algorithm alternately combined with random projection method are employed to generate diverse base partitions. Meanwhile, in order to improve the quality of base clustering partitions, we propose a new selection strategy for the number of clusters k in k-means algorithm. In the clustering selection process, both diversity and quality of the base clustering partitions are evaluated by multi-modal metrics from two levels: clustering labels and data structure. Based on five UCI benchmark datasets, experimental results demonstrate that the proposed method not only can generate but also select base clustering partitions with both diversity and quality. Experimental analyses show the validity and stability of the proposed scheme. |
| first_indexed | 2024-12-16T17:28:14Z |
| format | Article |
| id | doaj.art-5bf9d876be0d484483a6027fd5dadf83 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:28:14Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5bf9d876be0d484483a6027fd5dadf832022-12-21T22:23:00ZengIEEEIEEE Access2169-35362018-01-016641596416810.1109/ACCESS.2018.28776668506340Two-Level-Oriented Selective Clustering Ensemble Based on Hybrid Multi-Modal MetricsHongling Wang0https://orcid.org/0000-0001-5039-1298Gang Liu1School of Computer Science, China University of Geosciences, Wuhan, ChinaSchool of Computer Science, China University of Geosciences, Wuhan, ChinaThe purpose of selective clustering ensemble is to select a subset of base clustering partitions with predictive performance and combine these partitions into more accurate and stable final results. Traditional approaches tend to utilize the well-known validity criteria such as NMI to evaluate the quality and diversity of base clustering partitions in the selection process. However, the characteristics of the original data and the data structure itself are commonly neglected. Furthermore, the generation process of base clustering partitions is more concerned with diversity and less consideration of quality. To tackle these problems, we propose a new selective clustering ensemble scheme. In the process of generating base clustering partitions, k-means and hierarchical clustering algorithm alternately combined with random projection method are employed to generate diverse base partitions. Meanwhile, in order to improve the quality of base clustering partitions, we propose a new selection strategy for the number of clusters k in k-means algorithm. In the clustering selection process, both diversity and quality of the base clustering partitions are evaluated by multi-modal metrics from two levels: clustering labels and data structure. Based on five UCI benchmark datasets, experimental results demonstrate that the proposed method not only can generate but also select base clustering partitions with both diversity and quality. Experimental analyses show the validity and stability of the proposed scheme.https://ieeexplore.ieee.org/document/8506340/Diversitymulti-modal metricsqualityselective clustering ensemble |
| spellingShingle | Hongling Wang Gang Liu Two-Level-Oriented Selective Clustering Ensemble Based on Hybrid Multi-Modal Metrics IEEE Access Diversity multi-modal metrics quality selective clustering ensemble |
| title | Two-Level-Oriented Selective Clustering Ensemble Based on Hybrid Multi-Modal Metrics |
| title_full | Two-Level-Oriented Selective Clustering Ensemble Based on Hybrid Multi-Modal Metrics |
| title_fullStr | Two-Level-Oriented Selective Clustering Ensemble Based on Hybrid Multi-Modal Metrics |
| title_full_unstemmed | Two-Level-Oriented Selective Clustering Ensemble Based on Hybrid Multi-Modal Metrics |
| title_short | Two-Level-Oriented Selective Clustering Ensemble Based on Hybrid Multi-Modal Metrics |
| title_sort | two level oriented selective clustering ensemble based on hybrid multi modal metrics |
| topic | Diversity multi-modal metrics quality selective clustering ensemble |
| url | https://ieeexplore.ieee.org/document/8506340/ |
| work_keys_str_mv | AT honglingwang twolevelorientedselectiveclusteringensemblebasedonhybridmultimodalmetrics AT gangliu twolevelorientedselectiveclusteringensemblebasedonhybridmultimodalmetrics |