Mining latent patterns in geoMobile data via EPIC

Arvind Narayanan, Saurabh Verma, Zhi Li Zhang

Research output: Contribution to journalArticlepeer-review


We coin the term geoMobile data to emphasize datasets that exhibit geo-spatial features reflective of human behaviors. We propose and develop an EPIC framework to mine latent patterns from geoMobile data and provide meaningful interpretations: we first ‘E’xtract latent features from high dimensional geoMobile datasets via Laplacian Eigenmaps and perform clustering in this latent feature space; we then use a state-of-the-art visualization technique to ‘P’roject these latent features into 2D space; and finally we obtain meaningful ‘I’nterpretations by ‘C’ulling cluster-specific significant feature-set. We illustrate that the local space contraction property of our approach is most superior than other major dimension reduction techniques. Using diverse real-world geoMobile datasets, we show the efficacy of our framework via three case studies.

Original languageEnglish (US)
Pages (from-to)2771-2798
Number of pages28
JournalWorld Wide Web
Issue number6
StatePublished - Nov 1 2019

Bibliographical note

Funding Information:
This research was supported in part by DoD ARO MURI Award W911NF-12-1-0385, DTRA grant HDTRA1- 14-1-0040, NSF grant CNS-1411636, CNS-1618339 and CNS-1617729.

Publisher Copyright:
© 2019, Springer Science+Business Media, LLC, part of Springer Nature.


  • Data mining
  • Epic
  • Feature distributions
  • GeoMobile
  • Latent patterns
  • Regional patterns


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