Flexible Bivariate Beta Mixture Model: A Probabilistic Approach for Clustering Complex Data Structures
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909514710646784 |
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| author | Hsu, Yung-Peng Chen, Hung-Hsuan |
| author_facet | Hsu, Yung-Peng Chen, Hung-Hsuan |
| contents | Clustering is essential in data analysis and machine learning, but traditional algorithms like $k$-means and Gaussian Mixture Models (GMM) often fail with nonconvex clusters. To address the challenge, we introduce the Flexible Bivariate Beta Mixture Model (FBBMM), which utilizes the flexibility of the bivariate beta distribution to handle diverse and irregular cluster shapes. Using the Expectation Maximization (EM) algorithm and Sequential Least Squares Programming (SLSQP) optimizer for parameter estimation, we validate FBBMM on synthetic and real-world datasets, demonstrating its superior performance in clustering complex data structures, offering a robust solution for big data analytics across various domains. We release the experimental code at https://github.com/yung-peng/MBMM-and-FBBMM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_19938 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Flexible Bivariate Beta Mixture Model: A Probabilistic Approach for Clustering Complex Data Structures Hsu, Yung-Peng Chen, Hung-Hsuan Machine Learning Artificial Intelligence Clustering is essential in data analysis and machine learning, but traditional algorithms like $k$-means and Gaussian Mixture Models (GMM) often fail with nonconvex clusters. To address the challenge, we introduce the Flexible Bivariate Beta Mixture Model (FBBMM), which utilizes the flexibility of the bivariate beta distribution to handle diverse and irregular cluster shapes. Using the Expectation Maximization (EM) algorithm and Sequential Least Squares Programming (SLSQP) optimizer for parameter estimation, we validate FBBMM on synthetic and real-world datasets, demonstrating its superior performance in clustering complex data structures, offering a robust solution for big data analytics across various domains. We release the experimental code at https://github.com/yung-peng/MBMM-and-FBBMM. |
| title | Flexible Bivariate Beta Mixture Model: A Probabilistic Approach for Clustering Complex Data Structures |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2502.19938 |