A New Scalable and Efficient Parallel Algorithm (PRACAL) for Clustering Large Datasets

S.A. Salem and A.K. Nandi (UK)


Parallel processing, Data clustering, Unsupervised classifi cation, Retinal image segmentation, Computer clusters


Data clustering is a common technique for data analysis, which is used in many fields, including machine learn ing, data mining, pattern recognition, image analysis and bioinformatics. Due to the continuous increase of datasets size and the intensive computation of clustering algorithms when used for analyzing large datasets, developing of ef ficient clustering algorithms is needed for processing time reduction. This paper describes the design and implemen tation of a recently developed clustering algorithm RACAL [1], which is a RAdius based Clustering ALgorithm. The proposed parallel algorithm (PRACAL) has the ability to cluster large datasets of high dimensions in a reasonable time, which leads to a higher performance computing.

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