Improving penalized regression-based clustering model in big data

Clustering is the main procedure for data mining with a wide application such as gene analysis. Clustering is a method of separates (grouping) previously unclassified data on the basis of its features, and it is an unsupervised learning problem that divides that data into groups in such a way that i...

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Bibliographic Details
Published in:Journal of physics. Conference series 2021-05, Vol.1897 (1), p.12036
Main Authors: Mahmood Al-Kababchee, Sarah Ghanim, Qasim, Omar Saber, Algamal, Zakariya Yahya
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Data mining
Gene expression
Norms
Physics
Regression models
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Summary:Clustering is the main procedure for data mining with a wide application such as gene analysis. Clustering is a method of separates (grouping) previously unclassified data on the basis of its features, and it is an unsupervised learning problem that divides that data into groups in such a way that it makes those data in the same group more similar to each other compared to in other groups. Penalized regression-based clustering is an extension of the “Sum Of Norms” clustering model. In this paper, the nature-inspired algorithm is employed to improve the penalized regression-based clustering to better estimation. The real data application on gene expression data results suggests that our proposed improvement can bring significant improvement relative to others.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1897/1/012036