Validating the stable clustering of songs in a structured 3D SOM

A structured 3D SOM is an extension of a Self-Organizing Map from 2D to 3D in such a way that a pre-defined structure is built into the design of the 3D map. The structured 3D SOM is a 3×3×3 structure that has a distinct core cube in the center and exterior cubes around the core. The current application of the structured SOM, as a digital music archive, only uses the 8 corner cubes among the 26 exterior cubes. Given that the SOM has a built-in structure, the SOM learning algorithm is modified to include a four-phase learning and labeling phase. The first phase is meant to position the music files in their general locations within the core cube. The second phase positions the music files in their respective corner cubes according to their music genre. The second phase is therefore a semi-supervised version of the SOM algorithm which leads to the stability of the trained SOM in terms of the general distribution of the music files in the core cube. The third phase does a fine adjustment of the weight vectors in the core cube and finalizes the training of the 3D SOM. The final fourth phase is the labeling of the core cube and the association (uploading) of music files to specific nodes in the core cube. Based on the pre-defined structure of the 3D SOM, a precise measure is developed to measure the quality of the resulting trained SOM (in this case, the music archive), as well as the quality of the different categories/genres of music albums based on a novel measure of the distortion values of music files with respect to their respective music genres.

[1]  Arnulfo P. Azcarraga,et al.  Design of a Structured 3D SOM as a Music Archive , 2011, WSOM.

[2]  Garrison W. Cottrell,et al.  Non-Linear Dimensionality Reduction , 1992, NIPS.

[3]  Elias Pampalk,et al.  Content-based organization and visualization of music archives , 2002, MULTIMEDIA '02.

[4]  Michel Verleysen,et al.  Bridging Information Visualization with Machine Learning (Dagstuhl Seminar 15101) , 2015, Dagstuhl Reports.

[5]  Jarkko Venna,et al.  Neighborhood Preservation in Nonlinear Projection Methods: An Experimental Study , 2001, ICANN.

[6]  M. V. Velzen,et al.  Self-organizing maps , 2007 .

[7]  Thomas Villmann,et al.  Topology preservation in self-organizing feature maps: exact definition and measurement , 1997, IEEE Trans. Neural Networks.

[8]  Bart Baesens,et al.  A new SOM-based method for profile generation: Theory and an application in direct marketing , 2012, Eur. J. Oper. Res..

[9]  Michel Verleysen,et al.  On the Effects of Dimensionality on Data Analysis with Neural Networks , 2009, IWANN.

[10]  Shan Ling Pan,et al.  Extracting salient dimensions for automatic SOM labeling , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[11]  Marco Wiering,et al.  2011 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) , 2011, IJCNN 2011.

[12]  Michel Verleysen,et al.  Bridging Information Visualization with Machine Learning , 2015 .

[13]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[14]  Cory McKay,et al.  Automatic music classification with jmir , 2010 .

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

[16]  Peter Knees,et al.  An innovative three-dimensional user interface for exploring music collections enriched , 2006, MM '06.

[17]  Mark A. Hall,et al.  Correlation-based Feature Selection for Machine Learning , 2003 .

[18]  George Tzanetakis,et al.  Assistive music browsing using self-organizing maps , 2009, PETRA '09.

[19]  Timo Honkela,et al.  Websom for Textual Data Mining , 1999, Artificial Intelligence Review.