ACM Computing Surveys (CSUR)
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rough-fuzzy weighted k-nearest leader classifier for large data sets
Pattern Recognition
Rough-DBSCAN: A fast hybrid density based clustering method for large data sets
Pattern Recognition Letters
Speeding-up the kernel k-means clustering method: A prototype based hybrid approach
Pattern Recognition Letters
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The paper is about speeding-up the k-means clustering method which processes the data in a faster pace, but produces the same clustering result as the k-means method. We present a prototype based method for this where prototypes are derived using the leaders clustering method. Along with prototypes called leaders some additional information is also preserved which enables in deriving the k means. Experimental study is done to compare the proposed method with recent similar methods which are mainly based on building an index over the data-set.