Parallel Hybrid Clustering using Genetic Programming and Multi-Objective Fitness with Density (PYRAMID)

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Provided by: Nova Southeastern University
Topic: Big Data
Format: PDF
Clustering is the process of locating patterns in large data sets. It is an active research area that provides value to scientific as well as business applications. Practical clustering faces several challenges including: identifying clusters of arbitrary shapes, sensitivity to the order of input, dynamic determination of the number of clusters, outlier handling, processing speed of massive data sets, handling higher dimensions, and dependence on user-supplied parameters. Many studies have addressed one or more of these challenges. This paper proposes an algorithm called parallel hybrid clustering using genetic programming and multi-objective fitness with density (PYRAMID).
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