Python-based Graphical User Interface for Automatic Selection of Data Clustering Algorithm

Abstract

The process of grouping a set of similar data objects in the same group based on similarity criteria is called clustering. There are many clustering algorithms and software tools. Currently, K-means and Weka are the most common clustering algorithms and tools, respectively. The Weka tool does not contain all possible clustering algorithms and does not provide a comparative study between them to illustrate the differences and the suitable one for the dataset used. In this context, the main purpose of this paper is developing highly interactive graphical software application to make a comparative analysis of nine different clustering algorithms, including the K-means algorithm, Mean Shift algorithm, Affinity Propagation, and Density-based algorithm to choose the compatible one in terms of efficiency and accuracy. The simulation is done by a graphical user interface (GUI) software system designed by Python on a general data set. The limitations and directions for future research are also presented.

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