To combine steady-state genetic algorithm and ensemble learning for data clustering
Pattern Recognition Letters
Boosting for Model-Based Data Clustering
Proceedings of the 30th DAGM symposium on Pattern Recognition
A scalable framework for cluster ensembles
Pattern Recognition
Comparing hard and fuzzy c-means for evidence-accumulation clustering
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Video segmentation based on motion coherence of particles in a video sequence
IEEE Transactions on Image Processing
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Ensemble of clustering methods is recently shown to perform better than conventional clustering methods. One of the drawback of the ensemble is, its computational requirements can be very large and hence may not be suitable for large data sets. The paper presents an ensemble of leaders clustering methods where the entire ensemble requires only a single scan of the data set. Further, the component leaders complement each other while deriving individual partitions. A heuristic based consensus method to combine the individual partitions is presented and is compared with a well known consensus method called co-association based consensus. Experimentally the proposed methods are shown to perform well.