Robust growing neural gas algorithm with application in cluster analysis

• Published in: Neural networks Vol. 17; no. 8; pp. 1135 - 1148
• Main Authors: Qin, A.K., Suganthan, P.N.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 2004
• Subjects: Minimum description length; Outlier resistant; Prototypes; Robust clustering algorithm; Robust growing neural gas; Topology formation; Minimum description length; Outlier resistant; Robust clustering algorithm; Topology formation; Prototypes; Robust growing neural gas
• ISSN: 0893-6080; 1879-2782
• DOI: 10.1016/j.neunet.2004.06.013

We propose a novel robust clustering algorithm within the Growing Neural Gas (GNG) framework, called Robust Growing Neural Gas (RGNG) network. 1 1 The Matlab codes are available from http://www.ntu.edu.sg/home/EPNSugan. By incorporating several robust strategies, such as outlier resistant scheme, adaptive modulation of learning rates and cluster repulsion method into the traditional GNG framework, the proposed RGNG network possesses better robustness properties. The RGNG is insensitive to initialization, input sequence ordering and the presence of outliers. Furthermore, the RGNG network can automatically determine the optimal number of clusters by seeking the extreme value of the Minimum Description Length (MDL) measure during network growing process. The resulting center positions of the optimal number of clusters represented by prototype vectors are close to the actual ones irrespective of the existence of outliers. Topology relationships among these prototypes can also be established. Experimental results have shown the superior performance of our proposed method over the original GNG incorporating MDL method, called GNG-M, in static data clustering tasks on both artificial and UCI data sets.