Interactive probabilistic post-mining of user-preferred spatial co-location patterns

Lizhen Wang, Xuguang Bao, Longbing Cao

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

12 Citations (Scopus)


Spatial co-location pattern mining is an important task in spatial data mining. However, traditional mining frameworks often produce too many prevalent patterns of which only a small proportion may be truly interesting to end users. To satisfy user preferences, this work proposes an interactive probabilistic post-mining method to discover user-preferred co-location patterns from the early-round of mined results by iteratively involving user's feedback and probabilistically refining preferred patterns. We first introduce a framework of interactively post-mining preferred co-location patterns, which enables a user to effectively discover the co-location patterns tailored to his/her specific preference. A probabilistic model is further introduced to measure the user feedback-based subjective preferences on resultant co-location patterns. This measure is used to not only select sample co-location patterns in the iterative user feedback process but also rank the results. The experimental results on real and synthetic data sets demonstrate the effectiveness of our approach.

Original languageEnglish
Title of host publicationIEEE 34th International Conference on Data Engineering
Subtitle of host publicationICDE 2018 : proceedings
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages4
ISBN (Electronic)9781538655207
ISBN (Print)9781538655214
Publication statusPublished - 2018
Externally publishedYes
Event34th IEEE International Conference on Data Engineering, ICDE 2018 - Paris, France
Duration: 16 Apr 201819 Apr 2018

Publication series

ISSN (Print)1063-6382
ISSN (Electronic)2375-026X


Conference34th IEEE International Conference on Data Engineering, ICDE 2018


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