Data mining approach to predicting user’s past location (데이터마이닝 분류기를 활용한 사용자의 과거 장소예측에 관한 연구)

LeeEunMin (이은민); Kun-Chang Lee (이건창)

doi:10.9708/jksci.2017.22.11.97

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, Kun-Chang Lee 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.

KEYWORDS

Location prediction, Data mining, Classifiers, Bayesian network, Contextual dataset

Citation status

* References for papers published after 2023 are currently being built.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Data mining approach to predicting user’s past location

ABSTRACT

KEYWORDS

Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Data mining approach to predicting user’s past location

ABSTRACT

KEYWORDS

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REFERENCES (30) * References for papers published after 2023 are currently being built.

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* References for papers published after 2023 are currently being built.