Flexible Bivariate Beta Mixture Model: A Probabilistic Approach for Clustering Complex Data Structures
This provides a robust clustering solution for big data analytics across various domains, though it is incremental as it builds on existing mixture model frameworks.
The paper tackled the problem of clustering nonconvex and irregular data structures by introducing the Flexible Bivariate Beta Mixture Model (FBBMM), which demonstrated superior performance on synthetic and real-world datasets.
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.