BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Rough-fuzzy weighted k-nearest leader classifier for large data sets
Pattern Recognition
Weighted k-nearest leader classifier for large data sets
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
RSMAT: Robust simultaneous modeling and tracking
Pattern Recognition Letters
A novel possibilistic fuzzy leader clustering algorithm
International Journal of Hybrid Intelligent Systems - Rough and Fuzzy Methods for Data Mining
High scent web page recommendations using fuzzy rough set attribute reduction
Transactions on rough sets XIV
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Speeding-up the kernel k-means clustering method: A prototype based hybrid approach
Pattern Recognition Letters
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In this paper, an efficient hierarchical clustering algorithm, suitable for large data sets is proposed for effective clustering and prototype selection for pattern classification. It is another simple and efficient technique which uses incremental clustering principles to generate a hierarchical structure for finding the subgroups/subclusters within each cluster. As an example, a two level clustering algorithm--'Leaders-Subleaders', an extension of the leader algorithm is presented. Classification accuracy (CA) obtained using the representatives generated by the Leaders-Subleaders method is found to be better than that of using leaders as representatives. Even if more number of prototypes are generated, classification time is less as only a part of the hierarchical structure is searched.