Investigation of Data of a Metropolitan Municipality Call Center by Cluster Analysis

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One of the most important issues in statistical data analysis is big data. The process of accessing useful information from big data is called data mining. In clustering analysis, which is one of the data mining methods, via different algorithms, it is desirable to include similar data in the same cluster and divergent data in different clusters. Hierarchical clustering and k-means methods are the most commonly used algorithms in clustering analysis. In this study, call center data of five districts in a metropolitan municipality with a high population density in Turkey were examined. Call center data were clustered according to seven different variables by k-means method. These variables are respective department, application type, application district, education level, gender, age and instant solution. SPSS Clementine and WEKA which are the data mining package programs were used for the analysis and the results were compared and interpreted.

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Call Center , Clustering Analysis , K , means Method , Municipality

Erciyes University Journal Of The Institute Of Science an‎d Technology

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Erciyes University Journal Of The Institute Of Science an‎d Technology