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Cognitive Mining for Exploratory Data Analytics Using Clustering Based on Particle Swarm Optimization: Cognitive Mining for Exploratory Data Analytics
Abstract
This chapter examines the exploratory data analytics that require statistical techniques on data sets which are in the form of object-attribute-time format and referred to as three-dimensional data sets. It is very difficult to cluster and hence a subspace clustering method is used. Existing algorithms like CATSeeker are not actionable and its 3D structure complicates the clustering process, hence they are inadequate to solve this clustering problem. To cluster these three-dimensional data sets, a new centroid-based concept is introduced in the proposed system called clustering using particle swarm optimization (CPSO). This CPSO framework can be applied to financial and stock domain datasets through the unique combination of (1) singular value decomposition (SVD), (2) particle swarm optimization (PSO), and (3) 3D frequent item set mining which results in efficient performance. CPSO framework prunes the entire search space to identify the significant subspaces and clusters the datasets based on optimal centroid value.
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