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Data Mining Approach Based on Hierarchical Gaussian Mixture Representation Model

Hanan A. Hosni Mahmoud1,*, Alaaeldin M. Hafez2, Fahd Althukair3

1 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh, 11671, Saudi Arabia
2 Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
3 Department of Electrical Engineering and Computer Sciences, College of Engineering, University of California, Berkeley, USA

* Corresponding Author: Hanan A. Hosni Mahmoud. Email: email

Intelligent Automation & Soft Computing 2023, 35(3), 3727-3741.


Infinite Gaussian mixture process is a model that computes the Gaussian mixture parameters with order. This process is a probability density distribution 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 modelling that use prior knowledge of the representation hyper-parameters and approximate 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 Arabic patterns. Experiments are also performed on the application of facial expression recognition and prove the accuracy of the proposed method using a benchmark dataset which contains eight different facial expressions.


Cite This Article

H. A. H. Mahmoud, A. M. Hafez and F. Althukair, "Data mining approach based on hierarchical gaussian mixture representation model," Intelligent Automation & Soft Computing, vol. 35, no.3, pp. 3727–3741, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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