Performance Improvement Through Parallelization of Graph Clustering Algorithm

Provided by: International Journal of Computer Applications
Topic: Big Data
Format: PDF
Clustering is defined as dividing elements in to groups called clusters. Clustering task has been used in many fields including image/video processing, machine learning, data mining, biochemistry and bioinformatics etc. Different types of clustering algorithms have been developed like partitional, hierarchical, graph-based clustering etc. according to the properties of the data to be clustered. Most of the Clustering algorithm involves iterative or recursive function calls to find locally and globally optimal solution from a high dimensional data set.

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