Feature selection for ensembles
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
ACM Computing Surveys (CSUR)
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ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Neural Computation
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HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
Moderate diversity for better cluster ensembles
Information Fusion
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The Journal of Machine Learning Research
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Pattern Recognition Letters
Bagging-based spectral clustering ensemble selection
Pattern Recognition Letters
| Hi-index | 0.10 |
Traditional clustering ensembles methods combine all obtained clustering results at hand. However, we observe that it can often achieve a better clustering solution if only part of all available clustering results are combined. This paper proposes a novel clustering ensembles method, termed as resampling-based selective clustering ensembles method. The proposed selective clustering ensembles method works by evaluating the qualities of all obtained clustering results through resampling technique and selectively choosing part of promising clustering results to build the ensemble committee. The final solution is obtained through combining the clustering results of the ensemble committee. Experimental results on several real data sets demonstrate that resampling-based selective clustering ensembles method is often able to achieve a better solution when compared with traditional clustering ensembles methods.