Unsupervised Weight-Based Cluster Labeling for Self-Organizing Maps

Self-organizing maps (SOMs) have been applied for practical data analysis, in the contexts of exploratory data analysis (EDA) and data mining (DM). Many SOM-based EDA and DM techniques require that descriptive labels be applied to a SOM’s neurons. Several techniques exist for labeling SOM neurons in a supervised fashion, using classification information associated with a set of labeling data examples. However, classification information is often unavailable, necessitating the use of unsupervised labeling approaches that do not require pre-classified labeling data. This paper surveys existing unsupervised neuron labeling techniques. A novel unsupervised labeling algorithm, namely unsupervised weight-based cluster labeling, is described and critically discussed. The proposed method labels emergent neuron clusters using sub-labels built from statistically significant weights. Visualizations of the labelings produced by a prototype of the proposed approach are presented.

[1]  Andries Petrus Engelbrecht,et al.  HybridSOM: A generic rule extraction framework for self-organizing feature maps , 2009, 2009 IEEE Symposium on Computational Intelligence and Data Mining.

[2]  Casimir C. Klimasauskas.  Neural networks for , 1996 .

[3]  A. Corradini,et al.  A hybrid stochastic-connectionist architecture for gesture recognition , 1999, Proceedings 1999 International Conference on Information Intelligence and Systems (Cat. No.PR00446).

[4]  Sandhya Samarasinghe,et al.  Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition , 2006 .

[5]  T. Kohonen,et al.  Bibliography of Self-Organizing Map SOM) Papers: 1998-2001 Addendum , 2003 .

[6]  Shan Ling Pan,et al.  Improved SOM Labeling Methodology for Data Mining Applications , 2008, Soft Computing for Knowledge Discovery and Data Mining.

[7]  Fionn Murtagh,et al.  Interpreting the Kohonen self-organizing feature map using contiguity-constrained clustering , 1995, Pattern Recognit. Lett..

[8]  Esa Alhoniemi.  Analysis of pulping data using the self-organizing map , 2000 .

[9]  Carlos Serrano-Cinca,et al.  Self organizing neural networks for financial diagnosis , 1996, Decision Support Systems.

[10]  Jian Pei,et al.  2012- Data Mining. Concepts and Techniques, 3rd Edition.pdf , 2012 .

[11]  Olli Simula,et al.  Interpretation and Comparison of Multidimensional Data Partitions , 2001 .

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

[13]  Ben Shneiderman,et al.  Readings in information visualization - using vision to think , 1999 .

[14]  Donald Ervin Knuth,et al.  The Art of Computer Programming , 1968 .

[15]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[16]  Arno Siebes,et al.  Data Mining: the search for knowledge in databases. , 1994 .

[17]  Timo Honkela,et al.  BIBLIOGRAPHY OF SELF-ORGANIZING MAP (SOM) PAPERS: 2002-2005 ADDENDUM , 2009 .

[18]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[19]  Andries Petrus Engelbrecht,et al.  A comparison of map neuron labeling approaches for unsupervised self-organizing feature maps , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[20]  Samuel Kaski,et al.  Bibliography of Self-Organizing Map (SOM) Papers: 1981-1997 , 1998 .

[21]  Olli Simula,et al.  An approach to automated interpretation of SOM , 2001, WSOM.

[22]  T. Kohonen.  Self-organized formation of topographically correct feature maps , 1982 .

[23]  Teuvo Kohonen,et al.  Visual Explorations in Finance , 1998 .

[24]  Jan D. Miller,et al.  Agglomeration and magnetic deinking for office paper , 2000 .

[25]  Esa Alhoniemi,et al.  Clustering of the self-organizing map , 2000, IEEE Trans. Neural Networks Learn. Syst..

[26]  Ning Zhong,et al.  Methodologies for Knowledge Discovery and Data Mining , 2002, Lecture Notes in Computer Science.

[27]  T. Kohonen,et al.  Visual Explorations in Finance with Self-Organizing Maps , 1998 .

[28]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[29]  Andreas Rauber,et al.  Automatic Labeling of Self-Organizing Maps: Making a Treasure-Map Reveal Its Secrets , 1999, PAKDD.

[30]  Olli Simula,et al.  Interpretation and comparison of multivariate data partitions , 2001, ESANN.

[31]  T. Kohonen.  Self-Organized Formation of Correct Feature Maps , 1982 .