A decision support system using demographic issues: a case study in turkey

The demographic distribution of people by cities is an important parameter to address the people’s behaviour. To distinguish people behaviour is useful for companies to understand the customer behaviour. In this article, a case study covering all 81 cities in Turkey and measuring 35 topics for each of them is handled. By using these topics and cities, it is investigated that how the cities are clustered. Because its efficiency, the Agglomerative hierarchical clustering and the Kmedoids clustering methods in rapidminer data mining software are used to cluster the data. To measure the efficiency of the agglomerative clustering algorithm, the Cophenetic Correlation Coefficient (CPCC) is used. After clustering, the results are inserted into a geographic information system to depict the results in a Turkey map. The results show that, the cities distributed in the same geographical areas are in the same clusters with some exempts. On the other hand, some cities those are in different provinces show the same behaviour. The results of the study can also be used as a decision support system for a customer relations management.

[1]  Pece V Gorsevski,et al.  Integrating multi-criteria evaluation techniques with geographic information systems for landfill site selection: a case study using ordered weighted average. , 2012, Waste management.

[2]  M. Hossain,et al.  Customer perception on service quality in retail banking in Middle East: the case of Qatar , 2009 .

[3]  Roung-Shiunn Wu,et al.  Customer segmentation of multiple category data in e-commerce using a soft-clustering approach , 2011, Electron. Commer. Res. Appl..

[4]  Zhaohua Deng,et al.  Understanding customer satisfaction and loyalty: An empirical study of mobile instant messages in China , 2010, Int. J. Inf. Manag..

[5]  Khairullah Khan,et al.  Identifying product features from customer reviews using hybrid patterns , 2014, Int. Arab J. Inf. Technol..

[6]  Ayse Pamuk,et al.  Geography of immigrant clusters in global cities: a case study of San Francisco , 2004 .

[7]  Norman M. Sadeh,et al.  The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City , 2012, ICWSM.

[8]  Tagaram Soni Madhulatha,et al.  Comparison between K-Means and K-Medoids Clustering Algorithms , 2011 .

[9]  J. Musso,et al.  The Political Economy of City Formation in California: Limits to Tiebout Sorting , 2001 .

[10]  Nina Schwarz.  Urban form revisited—Selecting indicators for characterising European cities , 2010 .

[11]  Wen-Yu Chiang,et al.  To mine association rules of customer values via a data mining procedure with improved model: An empirical case study , 2011, Expert Syst. Appl..

[12]  Andrew Kusiak,et al.  Data Mining and Warehousing in Pharma Industry , 2005 .

[13]  R. Suganya,et al.  Data Mining Concepts and Techniques , 2010 .

[14]  SAS Global Forum 2012 Data Mining and Text Analytics , 2022 .

[15]  Giovanni Felici,et al.  Mathematical Methods for Knowledge Discovery and Data Mining , 2007 .

[16]  Arthur Getis,et al.  Spatial statistical analysis and geographic information systems , 1992 .

[17]  Wei-Jaw Deng,et al.  The relationships among service quality, perceived value, customer satisfaction, and post-purchase intention in mobile value-added services , 2009, Comput. Hum. Behav..