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Data mining approach to predicting user’s past location

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2017, 22(11), pp.97-104
  • DOI : 10.9708/jksci.2017.22.11.97
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : March 14, 2017
  • Accepted : August 28, 2017
  • Published : November 30, 2017

LeeEunMin 1 LEE, KUN CHANG 1

1성균관대학교

Accredited

ABSTRACT

Location prediction has been successfully utilized to provide high quality of location-based services to customers in many applications. In its usual form, the conventional type of location prediction is to predict future locations based on user's past movement history. However, as location prediction needs are expanded into much complicated cases, it becomes necessary quite frequently to make inference on the locations that target user visited in the past. Typical cases include the identification of locations that infectious disease carriers may have visited before, and crime suspects may have dropped by on a certain day at a specific time-band. Therefore, primary goal of this study is to predict locations that users visited in the past. Information used for this purpose include user's demographic information and movement histories. Data mining classifiers such as Bayesian network, neural network, support vector machine, decision tree were adopted to analyze 6868 contextual dataset and compare classifiers' performance. Results show that general Bayesian network is the most robust classifier.

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