Optimal Centroid Estimation Scheme for Multi Dimensional Clustering

High dimensional data values are processed and optimized with feature selection process. A feature selection algorithm is constructed with the consideration of efficiency and effectiveness factors. The efficiency concerns the time required to find a subset of features. The effectiveness is related to the quality of the subset of features. 3 dimensional data models are constructed with object, attribute and context information. Cluster quality is decided with domain knowledge and parameter setting requirements. CAT Seeker is a centroid-based actionable 3D subspace clustering framework. CAT Seeker framework is used to find profitable actions. Singular value decomposition, numerical optimization and 3D frequent itemset mining methods are integrated in CAT Seeker model. Singular value decomposition (SVD) is used to calculating and pruning the homogeneous tensor. Augmented Lagrangian Multiplier Method is used to calculating the probabilities of the values. 3D closed pattern mining is used to fetch Centroid-Based Actionable 3D Subspaces (CATS). Optimal centroid estimation scheme is used to improve the financial data analysis process.. Intra cluster accuracy factor is used to fetch centroid values. Inter cluster distance is also considered in centroid estimation process. Dimensionality analysis is applied to improve the subspace selection process.