In this paper, we propose a fast, memory-efficient, and scalable clustering algorithm for analyzing transactional data. Our approach has three unique features. First, we use the c...
The problem of finding clusters in data is challenging when clusters are of widely differing sizes, densities and shapes, and when the data contains large amounts of noise and out...
Motivated by the poor performance (linear complexity) of the EM algorithm in clustering large data sets, and inspired by the successful accelerated versions of related algorithms l...
Many applications require the management of spatial data. Clustering large spatial databases is an important problem which tries to find the densely populated regions in the featu...
P2P systems represent a large portion of the Internet traffic which makes the data discovery of great importance to the user and the broad Internet community. Hence, the power of ...