Data Mining Approach Based on Hierarchical Gaussian Mixture Representation Model

摘要

Infinite Gaussian mixture process is a model that computes the Gaus-sian mixture parameters with order.This process is a probability density distribu-tion with adequate training data that can converge to the input density curve.In this paper,we propose a data mining model namely Beta hierarchical distribution that can solve axial data modeling.A novel hierarchical Two-Hyper-Parameter Poisson stochastic process is developed to solve grouped data modelling.The solution uses data mining techniques to link datum in groups by linking their components.The learning techniques are novel presentations of Gaussian model-ling that use prior knowledge of the representation hyper-parameters and approx-imate them in a closed form.Experiments are performed on axial data modeling of Arabic Script classification and depict the effectiveness of the proposed method using a hand written benchmark dataset which contains complex handwritten Ara-bic patterns.Experiments are also performed on the application of facial expres-sion recognition and prove the accuracy of the proposed method using a benchmark dataset which contains eight different facial expressions.