Websom for Textual Data Mining
Artificial Intelligence Review - Special issue on data mining on the Internet
Self-Organizing Maps
Content-based organization and visualization of music archives
Proceedings of the tenth ACM international conference on Multimedia
Neighborhood Preservation in Nonlinear Projection Methods: An Experimental Study
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
An innovative three-dimensional user interface for exploring music collections enriched
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Assistive music browsing using self-organizing maps
Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Automatic music classification with jmir
Automatic music classification with jmir
Topology preservation in self-organizing feature maps: exact definition and measurement
IEEE Transactions on Neural Networks
Towards providing music for academic and leisurely activities of computer users
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
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A structured 3D SOM is an extension of a Self-Organizing Map from 2D to 3D where a structure has been built into the design of the 3D map. The 3D SOM is a 3×3×3 cube, with a distinct core cube in the center, and 26 exterior cubes around the center. The structured SOM mainly uses the 8 corner cubes among the 26 exterior cubes. Used to build a music archive, the SOM learning algorithm is modified to include a four-step learning and labeling phase. The first phase is meant only to position the music files in their general locations within the core cube. The second phase is meant to position the music files in their respective corner cubes. The third phase is meant to do a fine adjustment of the weight vectors in the core cube. The fourth phase is the labeling of the map and the association of music files to specific nodes in the map. Through the embedded 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 attraction index and the fidelity of music files to their respective music genres.