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An attempt to classify Turkish district data : K-Means and Self-Organizing Map (SOM) algorithms

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2004
Aksoy, Ece
There is no universally applicable clustering technique in discovering the variety of structures display in data sets. Also, a single algorithm or approach is not adequate to solve every clustering problem. There are many methods available, the criteria used differ and hence different classifications may be obtained for the same data. While larger and larger amounts of data are collected and stored in databases, there is increasing the need for efficient and effective analysis methods. Grouping or classification of measurements is the key element in these data analysis procedures. There are lots of non-spatial clustering techniques in various areas. However, spatial clustering techniques and software are not so common. This thesis is an attempt to classify Turkish district data with the help of two clustering algorithms: K-means clustering and self organizing maps (SOM). With the help of these two common techniques it is expected that a clustering can be reached, which can be used for different aims such as regional politics, constructing statistical integrity or analyzing distribution of funds, for same data in GIS environment and putting forward the facilitative usage of GIS in regional and statistical studies. All districts of Turkey, which is 923 units, were chosen as an application area in this thesis. Some limitations such as population were specified for clustering of Turkey̕s districts. Firstly, different clustering techniques for spatial classification were researched. K-Means and SOM algorithms were chosen to compare different methods with Turkey̕s district data. Afterward, database of Turkey̕s statistical datum was formed and analyzed joining with geographical data in the GIS environment. Different clustering software, ArcGIS, CrimeStat and Matlab, were applied according to conclusion of clustering techniques research. Self Organizing Maps (SOM) algorithm,