用双变量贝塔混合模型解决非凸聚类难题，适合复杂数据形状。
Flexible Bivariate Beta Mixture Model: A Probabilistic Approach for Clustering Complex Data Structures
- 基于双变量贝塔分布建模，灵活适应不规则簇形。
- 在合成与真实数据集上表现优于传统方法，聚类效果更优。
- 代码开源，适合大数据分析与复杂结构聚类任务。
聚类在数据分析与机器学习中至关重要，但传统算法如k-means和高斯混合模型（GMM）在处理非凸簇时表现不佳。为应对这一挑战，本文提出灵活的双变量贝塔混合模型（FBBMM），利用双变量贝塔分布的灵活性来捕捉多样且不规则的簇结构。通过期望最大化（EM）算法与序列最小二乘规划（SLSQP）优化器进行参数估计，FBBMM在合成数据和真实数据集上均表现出色，显著提升复杂数据结构的聚类性能，为各领域的大型数据分析提供稳健解决方案。实验代码已开源：https://github.com/yung-peng/MBMM-and-FBBMM。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。