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Cluster analysis has been widely applied in many areas such as data mining, geographical data processing, medicine, classification of statistical findings in social studies and so on. Most of these domains deal with massive collections of data. Hence the methods to handle them must be e cient both in terms of the number of data set scans and memory usage.

Several algorithms have been proposed in the literature for clustering large data sets viz; CLARANS [1], DB-SCAN [1], CURE [1], K-Means [2], etc. Most of these require more than one pass through the data set to find the required abstraction. Hence they are computationally expensive for the clustering of large data sets. Even though we have a single pass clustering algorithm called BIRCH [1], it uses a memory expensive data structure called CF tree. In this scenario the Leader algorithm [3], which requires only a single data set scan and less memory, turns out …