Performance evaluation of prototype‐based clustering algorithms combined MDL index - Aljobouri, Hadeel K.; Jaber, Hussain A.; Çankaya, Ilyas (Computer applications in engineering education, 2017)
Clustering algorithms are used to group data depending on a distance. Best clustering analysis should be resisting the presence of outliers, less sensitive to initialization as well as the input sequence ordering. This article compares the performance among three of prototype‐based unsupervised clustering algorithms: Neural Gas (NG), Growing Neural Gas (GNG) and Robust Growing Neural Gas (RGNG). Based on NG and GNG, there are different clustering algorithms proposed and suggested in different literatures. So, in this work a comparison between the two basic clustering algorithms NG and GNG have presented using the performance evaluation of these techniques, in contrast to the RGNG which was proposed within the GNG. Another comparison due to the MDL criterion between RGNG that 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 fed to the synthetic 2D dataset. Moreover, a simple software package is designed and implemented as an automatic clustering model for any dataset to use as a part of the neural network course. NG, GNG and RGNG algorithms are performed in the same package using a MATLAB‐based Graphical User Interface (GUI) tool. This visual tool lets the students/ researchers visualize the desired results using plots also clicking a few buttons.