Clustering algorithms with kernel functions

The clustering analysis is a method of data mining that aims to examine the properties of data and separates them into groups according to similarities. In this method, the similarities and differences between the data points are determined by using various measures and data set is divided into certain clusters. In this thesis, it is expressed kernel k-means clustering problem which want to find solving of the clustering problem. To solve this problem, it is investigated k-means algorithm and kernelk-means algorithm. These algorithms are coded in C# programming language and computation experiments are performed on various data sets. A comparative analysis of the kernel functional approaches and the standard clustering methods was performed.