A Data-Clustering Algorithm on Distributed Memory Multiprocessors
Revised Papers from Large-Scale Parallel Data Mining, Workshop on Large-Scale Parallel KDD Systems, SIGKDD
Programming Erlang: Software for a Concurrent World
Programming Erlang: Software for a Concurrent World
WSEAS Transactions on Information Science and Applications
Parallel Clustering Algorithms for Image Processing on Multi-core CPUs
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 03
Parallel K-Means Clustering Based on MapReduce
CloudCom '09 Proceedings of the 1st International Conference on Cloud Computing
Knowledge induction from medical databases with higher-order programming
WSEAS Transactions on Information Science and Applications
Parallel k-means clustering algorithm on DNA dataset
PDCAT'04 Proceedings of the 5th international conference on Parallel and Distributed Computing: applications and Technologies
Weighted k-means for density-biased clustering
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
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Multi-core processors have recently been available on most personal computers. To get the maximum benefit of computational power from the multi-core architecture, we need a new design on existing algorithms and software. In this paper we propose the parallelization of the well-known k-means clustering algorithm. We employ a single program multiple data (SPMD) approach based on a message passing model. Sending and receiving messages between a master and the concurrently created process are done in an asynchronous manner. Therefore, the implementation can be highly parallel and fault tolerant. The experimental results demonstrate considerable speedup rate of the proposed parallel k-means clustering method, compared to the serial k-means approach.