Toward mining user traversal patterns in the indoor environment

Shan Yun Teng, Tzu Yuan Chung, Kun Ta Chuang, Wei Shinn Ku

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

Abstract

We in this paper explore a new mining paradigm, called Indoor Traversal Patterns (abbreviated as ITP), to discover user traversal behavior in the mall-like indoor environment. The ITP algorithm can identify user traversal sequences from uncertain user itineraries with the RFID-based indoor positioning technology. Note that it is a highly challenging issue in the indoor environment to retrieve the precise locations in the indoor environment. Since previous works on mining user moving patterns usually rely on the precise spatiotemporal information from GPS signals, it is difficult to apply similar approaches to discover user traversal behavior in the indoor environment. We therefore develop a framework to transform the RFID antenna data to uncertain user traversal transactions, and further diminish the uncertainty before mining the indoor traversal patterns. Our experimental studies show that the proposed ITP algorithm can effectively overcome the impact from location uncertainty and discover high-quality traversal patterns, to provide insightful observation for marketing decision.

Original languageEnglish
Title of host publicationTrends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops
Subtitle of host publicationDANTH, BDM, MobiSocial, BigEC, CloudSD, MSMV-MBI, SDA, DMDA-Health, ALSIP, SocNet, DMBIH, BigPMA, Revised Selected Papers
EditorsWen-Chih Peng, Haixun Wang, Zhi-Hua Zhou, Tu Bao Ho, Vincent S. Tseng, Arbee L.P. Chen, James Bailey
PublisherSpringer Verlag
Pages677-688
Number of pages12
ISBN (Electronic)9783319131856
DOIs
Publication statusPublished - 2014 Jan 1
EventInternational Workshops on Data Mining and Decision Analytics for Public Health, Biologically Inspired Data Mining Techniques, Mobile Data Management, Mining, and Computing on Social Networks, Big Data Science and Engineering on E-Commerce, Cloud Service Discovery, MSMV-MBI, Scalable Dats Analytics, Data Mining and Decision Analytics for Public Health and Wellness, Algorithms for Large-Scale Information Processing in Knowledge Discovery, Data Mining in Social Networks, Data Mining in Biomedical informatics and Healthcare, Pattern Mining and Application of Big Data in conjunction with 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014 - Tainan, Taiwan
Duration: 2014 May 132014 May 16

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8643
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherInternational Workshops on Data Mining and Decision Analytics for Public Health, Biologically Inspired Data Mining Techniques, Mobile Data Management, Mining, and Computing on Social Networks, Big Data Science and Engineering on E-Commerce, Cloud Service Discovery, MSMV-MBI, Scalable Dats Analytics, Data Mining and Decision Analytics for Public Health and Wellness, Algorithms for Large-Scale Information Processing in Knowledge Discovery, Data Mining in Social Networks, Data Mining in Biomedical informatics and Healthcare, Pattern Mining and Application of Big Data in conjunction with 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014
CountryTaiwan
CityTainan
Period14-05-1314-05-16

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Teng, S. Y., Chung, T. Y., Chuang, K. T., & Ku, W. S. (2014). Toward mining user traversal patterns in the indoor environment. In W-C. Peng, H. Wang, Z-H. Zhou, T. B. Ho, V. S. Tseng, A. L. P. Chen, & J. Bailey (Eds.), Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops: DANTH, BDM, MobiSocial, BigEC, CloudSD, MSMV-MBI, SDA, DMDA-Health, ALSIP, SocNet, DMBIH, BigPMA, Revised Selected Papers (pp. 677-688). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8643). Springer Verlag. https://doi.org/10.1007/978-3-319-13186-3_60