Temporal Exceptional Model Mining Using Dynamic Bayesian Networks

Marcos L. P. Bueno, A.J. Hommersom, Peter J. F. Lucas

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

Abstract

The discovery of subsets of data that are characterized by models that differ significantly from the entire dataset, is the goal of exceptional model mining. With the increasing availability of temporal data, this task has clear relevance in discovering deviating temporal subprocesses that can bring insight into industrial processes, medical treatments, etc. As temporal data is often noisy, high-dimensional and has complex statistical dependencies, discovering such temporal subprocesses is challenging for current exceptional model mining methods. In this paper, we introduce Temporal Exceptional Model Mining to capture multiple and complex relationships among temporal variables of a dataset in a principled way. Our contributions are as follows: (i) we define the new task of temporal exceptional model mining; (ii) we characterize the discovery of exceptional temporal submodels using dynamic Bayesian networks by means of a new distance measure, (iii) we introduce a search procedure for exceptional dynamic Bayesian networks optimized by properties of the proposed distance, and (iv) the practical value of the proposed method is demonstrated based on simulated data and process data of funding applications and by comparisons with other exceptional model mining methods.
Original languageEnglish
Title of host publicationAdvanced Analytics and Learning on Temporal Data
Subtitle of host publication5th ECML PKDD Workshop, AALTD 2020
EditorsV. Lemaire, S. Malinowski, A. Bagnall, Th. Guyet, R. Tavenard, G. Ifrim
PublisherSpringer International Publishing AG
Pages97-112
Number of pages16
ISBN (Electronic)978-3-030-65742-0
ISBN (Print)978-3-030-65741-3
DOIs
Publication statusPublished - 2020
Event5th ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data - Online, Ghent, Belgium
Duration: 18 Sep 202018 Sep 2020
https://project.inria.fr/aaltd20/

Publication series

SeriesLecture Notes in Computer Science (LNCS) series
ISSN0302-9743

Workshop

Workshop5th ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data
Abbreviated titleAALTD 2020
CountryBelgium
CityGhent
Period18/09/2018/09/20
Internet address

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