A Bayesian Mixture Model of Temporal Point Processes with Determinantal Point Process Prior
This work addresses overfitting in event sequence clustering for applications like temporal data analysis, though it appears incremental as it builds on existing mixture models with a novel prior.
The paper tackles the problem of overfitting and excessive cluster generation in unsupervised event sequence clustering by proposing a Bayesian mixture model with a Determinantal Point Process prior, which results in moderately fewer yet more diverse mixture components and achieves outstanding results across multiple evaluation metrics.
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.