STExNMF: Spatio-temporally exclusive topic discovery for anomalous event detection

Dear Sungbok Shin, Minsuk Choi, Jinho Choi, Scott Langevin, Christopher Bethune, Philippe Horne, Nathan Kronenfeld, Ramakrishnan Kannan, Barry Drake, Haesun Park, Jaegul Choo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)


Understanding newly emerging events or topics associated with a particular region of a given day can provide deep insight on the critical events occurring in highly evolving metropolitan cities. We propose herein a novel topic modeling approach on text documents with spatio-temporal information (e.g., when and where a document was published) such as location-based social media data to discover prevalent topics or newly emerging events with respect to an area and a time point. We consider a map view composed of regular grids or tiles with each showing topic keywords from documents of the corresponding region. To this end, we present a tilebased spatio-temporally exclusive topic modeling approach called STExNMF, based on a novel nonnegative matrix factorization (NMF) technique. STExNMF mainly works based on the two following stages: (1) first running a standard NMF of each tile to obtain general topics of the tile and (2) running a spatiotemporally exclusive NMF on a weighted residual matrix. These topics likely reveal information on newly emerging events or topics of interest within a region. We demonstrate the advantages of our approach using the geo-tagged Twitter data of New York City. We also provide quantitative comparisons in terms of the topic quality, spatio-temporal exclusiveness, topic variation, and qualitative evaluations of our method using several usage scenarios. In addition, we present a fast topic modeling technique of our model by leveraging parallel computing.

Original languageEnglish
Title of host publicationProceedings - 17th IEEE International Conference on Data Mining, ICDM 2017
EditorsGeorge Karypis, Srinivas Alu, Vijay Raghavan, Xindong Wu, Lucio Miele
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9781538638347
Publication statusPublished - 2017 Dec 15
Event17th IEEE International Conference on Data Mining, ICDM 2017 - New Orleans, United States
Duration: 2017 Nov 182017 Nov 21

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Conference17th IEEE International Conference on Data Mining, ICDM 2017
Country/TerritoryUnited States
CityNew Orleans

Bibliographical note

Funding Information:
This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. DOE and supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (No. NRF-2016R1C1B2015924). The DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (

Publisher Copyright:
© 2017 IEEE.


  • Anomaly detection
  • Event detection
  • Matrix factorization
  • Social network analysis
  • Topic modeling

ASJC Scopus subject areas

  • General Engineering


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