Outlier Detection in Location Based Systems By Using Fuzzy Clustering

Customer segmentation has been one of most important decision in marketing. In general, demographics of customers, monetary value of customer transactions, types of product/service customers use are the sources of segmentation process. In recent years, new technology enabled new sources of data. On of these new data are the customer location data collected from location based systems (LBS). By using these location data an improved customer insight can be provided to the companies. Segmentation is an important tool for creating customer insight but anomalies in LBS data can prevent a well formed segmentation. In this paper we propose a novel approach to outlier detection in LBS data by using fuzzy c-means algorithm

[1]  Enrique H. Ruspini,et al.  Numerical methods for fuzzy clustering , 1970, Inf. Sci..

[2]  Stathes Hadjiefthymiades,et al.  Time-optimized user grouping in Location Based Services , 2015, Comput. Networks.

[3]  Na Chen,et al.  Hierarchical hesitant fuzzy K-means clustering algorithm , 2014, Applied Mathematics-A Journal of Chinese Universities.

[4]  Wan-Shiou Yang,et al.  A location-aware recommender system for mobile shopping environments , 2008, Expert Syst. Appl..

[5]  Keun Ho Ryu,et al.  A method for predicting future location of mobile user for location-based services system , 2009, Comput. Ind. Eng..

[6]  B. R. Prasad Babu,et al.  A Novel Concept of MANET Architecture for Location Based Service Using Circular Data Aggregation Technique , 2014 .

[7]  S. Shyam Sundar,et al.  Customization in location-based advertising: Effects of tailoring source, locational congruity, and product involvement on ad attitudes , 2015, Comput. Hum. Behav..

[8]  Basar Öztaysi,et al.  In-store behavioral analytics technology selection using fuzzy decision making , 2018, J. Enterp. Inf. Manag..

[9]  G. Meera Gandhi,et al.  An Enhanced Fuzzy Clustering and Expectation Maximization Framework based Matching Semantically Similar Sentences , 2015 .

[10]  Xiao Zou,et al.  Leveraging location-based services for couponing and infomediation , 2015, Decis. Support Syst..

[11]  DeJiu Chen,et al.  Developing a Context-aware Architecture in DySCAS , 2007 .

[12]  Liang-Chu Chen,et al.  Building and evaluating a location-based service recommendation system with a preference adjustment mechanism , 2009, Expert Syst. Appl..

[13]  Francesco Ricci,et al.  Mobile Recommender Systems , 2010, J. Inf. Technol. Tour..

[14]  Guangtao Fu,et al.  Classified real-time flood forecasting by coupling fuzzy clustering and neural network , 2010 .

[15]  Alexander Zipf,et al.  Road-based travel recommendation using geo-tagged images , 2015, Comput. Environ. Urban Syst..

[16]  Ingrid Moerman,et al.  Pattern mining in tourist attraction visits through association rule learning on Bluetooth tracking data: A case study of Ghent, Belgium , 2014 .

[17]  Charalampos Konstantopoulos,et al.  Mobile recommender systems in tourism , 2014, J. Netw. Comput. Appl..

[18]  Basar Oztaysi,et al.  A Novel Approach to Segmentation Using Customer Locations Data and Intelligent Techniques , 2017 .

[19]  Keith Cheverst,et al.  Developing a context-aware electronic tourist guide: some issues and experiences , 2000, CHI.

[20]  Pierpaolo D'Urso,et al.  Bagged fuzzy clustering for fuzzy data: An application to a tourism market , 2015, Knowl. Based Syst..

[21]  Yung-Ming Li,et al.  A social recommender mechanism for location-based group commerce , 2014, Inf. Sci..

[22]  Basar Öztaysi,et al.  Supplier Evaluation Using Fuzzy Clustering , 2014, Supply Chain Management Under Fuzziness.

[23]  Mehmed Kantardzic,et al.  Data-Mining Concepts , 2011 .

[24]  B. Sheela Rani,et al.  Colour image segmentation using fuzzy clustering techniques and competitive neural network , 2011, Appl. Soft Comput..