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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xueen Wang Deqiang Han Chongzhao Han |
| Copyright Year | 2013 |
| Description | Author affiliation: Sch. of Electron. & Inf. Eng., Xian Jiaotong Univ., Xian, China (Xueen Wang; Deqiang Han; Chongzhao Han) |
| Abstract | Ensemble clustering have been attracting lots of attentions, which combining several base data partitions to generate a single consensus partition with improved stability and robustness. Diversity is critical for the success of ensemble clustering. To enhance this characteristic, a subset of cluster ensemble is selected by removing the redundant partitions. Combined with ranking and forward selection strategies, the significance of attribute defined in rough set theory is employed as a heuristic to find the subset of cluster ensemble. Experimental results on the UCI machine learning repository demonstrate that the proposed algorithm is feasible and effective. |
| Starting Page | 438 |
| Ending Page | 444 |
| File Size | 757985 |
| Page Count | 7 |
| File Format | |
| ISBN | 9786058631113 |
| e-ISBN | 9781479902842 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-07-09 |
| Publisher Place | Turkey |
| Access Restriction | Subscribed |
| Rights Holder | ISIF ( Intl Society of Information Fusi |
| Subject Keyword | Clustering algorithms Partitioning algorithms Diversity reception Set theory Glass Information entropy Lungs attribute significance ensemble clustering rough set feature selection |
| Content Type | Text |
| Resource Type | Article |
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