Solving cluster ensemble problems by bipartite graph partitioning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Combining partitions by probabilistic label aggregation
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
ACM Transactions on Knowledge Discovery from Data (TKDD)
Cumulative Voting Consensus Method for Partitions with Variable Number of Clusters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Knowledge-Based Systems
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Clustering ensembles combine different clustering solutions into a single robust and stable one. Most of existing methods become highly time-consuming when the data size turns to large. In this paper, we study the properties of the defined 'clustering fragment' and put forward a useful proposition. Solid proofs are presented with two widely used goodness measures for clustering ensembles. Finally, a new ensemble framework termed as fragment-based clustering ensembles is proposed. Theoretically, most of existing methods can be improved by adopting this framework. To evaluate the proposed framework, three new methods are introduced by bring three popular clustering ensemble methods into our framework. The experimental results on several public data sets show that the three introduced methods are greatly improved in computational complexity and also achieved better or similar accurate results than the original methods.