Cluster identification algorithms for spin models—sequential and parallel
Concurrency: Practice and Experience
Vector quantization and signal compression
Vector quantization and signal compression
Introduction to parallel computing: design and analysis of algorithms
Introduction to parallel computing: design and analysis of algorithms
Parallel computing: principles and practice
Parallel computing: principles and practice
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Communications of the ACM
A programmer's guide to ZPL
K-Harmonic Means - A Spatial Clustering Algorithm with Boosting
TSDM '00 Proceedings of the First International Workshop on Temporal, Spatial, and Spatio-Temporal Data Mining-Revised Papers
Scalable information extraction for web queries
International Journal of Computational Science and Engineering
Distributed data mining patterns and services: an architecture and experiments
Concurrency and Computation: Practice & Experience
Data weighing mechanisms for clustering ensembles
Computers and Electrical Engineering
Effects of resampling method and adaptation on clustering ensemble efficacy
Artificial Intelligence Review
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Fueled by advances in computer technology and online business, data collection is rapidly accelerating, as well as the importance of its analysis--data mining. Increasing database sizes strain the scalability of many data mining algorithms. Data clustering is one of the fundamental techniques in data mining solutions. The many clustering algorithms developed face new challenges with growing data sets. Algorithms with quadratic or higher computational complexity, such as agglomerative algorithms, drop out quickly. More efficient algorithms, such as K-Means EM with linear cost per iteration, still need work to scale up to large data sets. This paper shows that many parameter estimation algorithms, including K-Means, K-Harmonic Means and EM, can be recast without approximation in terms of Sufficient Statistics, yielding an superior speed-up efficiency. Estimates using today's workstations and local area network technology suggest efficient speed-up to several hundred computers, leading to effective scale-up for clustering hundreds of gigabytes of data. Implementation of parallel clustering has been done in a parallel programming language, ZPL. Experimental results show above 90% utilization.