{"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/classification-and-clustering-for","title":"Classification and clustering for observations of event time data using non-homogeneous Poisson process models","arxiv_id":"1703.02111","date":"2017-03-06","proceeding":null,"authors":["Duncan Barrack","Simon Preston"],"abstract":"Data of the form of event times arise in various applications. A simple model\nfor such data is a non-homogeneous Poisson process (NHPP) which is specified by\na rate function that depends on time. We consider the problem of having access\nto multiple independent observations of event time data, observed on a common\ninterval, from which we wish to classify or cluster the observations according\nto their rate functions. Each rate function is unknown but assumed to belong to\na finite number of rate functions each defining a distinct class. We model the\nrate functions using a spline basis expansion, the coefficients of which need\nto be estimated from data. The classification approach consists of using\ntraining data for which the class membership is known, to calculate maximum\nlikelihood estimates of the coefficients for each group, then assigning test\nobservations to a group by a maximum likelihood criterion. For clustering, by\nanalogy to the Gaussian mixture model approach for Euclidean data, we consider\nmixtures of NHPP and use the expectation-maximisation algorithm to estimate the\ncoefficients of the rate functions for the component models and group\nmembership probabilities for each observation. The classification and\nclustering approaches perform well on both synthetic and real-world data sets.\nCode associated with this paper is available at\nhttps://github.com/duncan-barrack/NHPP .","url_abs":"http://arxiv.org/abs/1703.02111v4","url_pdf":"http://arxiv.org/pdf/1703.02111v4.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":"classification-and-clustering-for","repo_url":"https://github.com/duncan-barrack/NHPP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}