Performance Assessment of Unsupervised Clustering Algorithms Combined MDL Index

Best clustering analysis should be resisting the presence of outliers and be less sensi- tive to initialization as well as the input sequence ordering. This chapter compares the performance among three of the unsupervised clustering algorithms: neural gas (NG), growing neural gas (GNG), and robust growing neural gas (RGNG). A complete expla-nation of NG and GNG algorithms is presented in the next comparison with RGNG. Another comparison due to the minimum description length (MDL) criterion between RGNG used MDL value as the clustering validity index versus GNG and NG combined with MDL. Statistical estimations are applied to explain the meaning of the output results when these algorithms are fed to the synthetic 2D dataset. The techniques introduced in this chapter are designed and implemented in a simple software package using a MATLAB-based graphical user interface (GUI) tool, which allows users to interact with the clustering techniques and output data easily.