{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-dirichlet-mixture-model-of-hawkes-processes","title":"A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering","arxiv_id":"1701.09177","date":"2017-01-31","proceeding":"NeurIPS 2017 12","authors":["Hongteng Xu","Hongyuan Zha"],"abstract":"We propose an effective method to solve the event sequence clustering\nproblems based on a novel Dirichlet mixture model of a special but significant\ntype of point processes --- Hawkes process. In this model, each event sequence\nbelonging to a cluster is generated via the same Hawkes process with specific\nparameters, and different clusters correspond to different Hawkes processes.\nThe prior distribution of the Hawkes processes is controlled via a Dirichlet\ndistribution. We learn the model via a maximum likelihood estimator (MLE) and\npropose an effective variational Bayesian inference algorithm. We specifically\nanalyze the resulting EM-type algorithm in the context of inner-outer\niterations and discuss several inner iteration allocation strategies. The\nidentifiability of our model, the convergence of our learning method, and its\nsample complexity are analyzed in both theoretical and empirical ways, which\ndemonstrate the superiority of our method to other competitors. The proposed\nmethod learns the number of clusters automatically and is robust to model\nmisspecification. Experiments on both synthetic and real-world data show that\nour method can learn diverse triggering patterns hidden in asynchronous event\nsequences and achieve encouraging performance on clustering purity and\nconsistency.","url_abs":"http://arxiv.org/abs/1701.09177v5","url_pdf":"http://arxiv.org/pdf/1701.09177v5.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-dirichlet-mixture-model-of-hawkes-processes","repo_url":"https://github.com/HongtengXu/Hawkes-Process-Toolkit","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"},{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"point-processes","task_name":"Point Processes"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1701.09177","atlas_url":"https://app.syntology.ai/?focus=1701.09177","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}