用确定性点过程先验改进事件序列聚类，自动识别最优簇数并提升多样性。
A Bayesian Mixture Model of Temporal Point Processes with Determinantal Point Process Prior
- 引入确定性点过程作为先验，抑制聚类过拟合
- 在真实与合成数据上减少簇数量并提升多样性
- 适用于神经网络等复杂模型，适合无监督事件分析
异步事件序列聚类旨在无监督地将相似事件序列分组。现有基于时间点过程的混合模型常因过拟合导致簇数量过多且缺乏多样性。为此，本文提出基于确定性点过程先验的贝叶斯混合时间点过程模型（TP²DP²），并设计基于条件吉布斯采样的高效后验推断算法。该框架具有灵活性，可自动识别潜在簇数，并准确分组特征相似的序列。它适用于多种参数化时间点过程，包括基于神经网络的模型。在合成与真实数据上的实验表明，所提方法能生成更少但更具多样性的混合成分，在多个评估指标上表现优异。
原文摘要 · Abstract (English)
Asynchronous event sequence clustering aims to group similar event sequences in an unsupervised manner. Mixture models of temporal point processes have been proposed to solve this problem, but they often suffer from overfitting, leading to excessive cluster generation with a lack of diversity. To overcome these limitations, we propose a Bayesian mixture model of Temporal Point Processes with Determinantal Point Process prior (TP$^2$DP$^2$) and accordingly an efficient posterior inference algorithm based on conditional Gibbs sampling. Our work provides a flexible learning framework for event sequence clustering, enabling automatic identification of the potential number of clusters and accurate grouping of sequences with similar features. It is applicable to a wide range of parametric temporal point processes, including neural network-based models. Experimental results on both synthetic and real-world data suggest that our framework could produce moderately fewer yet more diverse mixture components, and achieve outstanding results across multiple evaluation metrics.
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。