An improved multi-SOM algorithm

This paper proposes a clustering algorithm based on the Self Organizing Map(SOM) method. To find the optimal number of clusters, our algorithm uses the Davies Bouldin index which has not been used previously in the multi-SOM. The proposed algorithm is compared t o three clustering methods based on five databases. Results show that our algorithm is as performing as concurrent methods.

[1]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[2]  Jean-Charles Lamirel,et al.  Intelligent Patent Analysis through the Use of a Neural Network: Experiment of Multi-Viewpoint Analysis with the MultiSOM Model , 2003, ACL 2003.

[3]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[4]  J.-C. Lamirel,et al.  MultiSOM: a multimap extension of the SOM model. Application to information discovery in an iconographic context , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[5]  Damminda Alahakoon,et al.  Exploratory data analysis with Multi-Layer Growing Self-Organizing Maps , 2010, 2010 Fifth International Conference on Information and Automation for Sustainability.

[6]  Marie Cottrell,et al.  Advantages and drawbacks of the Batch Kohonen algorithm , 2002, ESANN.

[7]  Jean-Charles Lamirel,et al.  Using artificial neural networks for mapping of scienceand technology: A multi-self-organizing-maps approach , 2001, Scientometrics.

[8]  Toby Smith.  Adapting to Increasing Data Availability Using Multi-layered Self-Organising Maps , 2009, 2009 International Conference on Adaptive and Intelligent Systems.

[9]  S. Abdelhak,et al.  Application of Multi-SOM clustering approach to macrophage gene expression analysis. , 2009, Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases.

[10]  Tian Zhang,et al.  BIRCH: an efficient data clustering method for very large databases , 1996, SIGMOD '96.