Expert Systems with Applications: An International Journal
Multilayer SOM with tree-structured data for efficient document retrieval and plagiarism detection
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Topological tree clustering of social network search results
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Web feed clustering and tagging aggregator using topological tree-based self-organizing maps
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Probabilistic self-organizing maps for continuous data
IEEE Transactions on Neural Networks
Topological tree clustering of web search results
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
An analyst-adaptive approach to focused crawlers
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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We present a new method for content management and knowledge discovery using a topology-preserving neural network. The method, termed topological organization of content (TOC), can generate a taxonomy of topics from a set of unannotated, unstructured documents. The TOC consists of a hierarchy of self-organizing growing chains (GCs), each of which can develop independently in terms of size and topics. The dynamic development process is validated continuously using a proposed entropy-based Bayesian information criterion (BIC). Each chain meeting the criterion spans child chains, with reduced vocabularies and increased specializations. This results in a topological tree hierarchy, which can be browsed like a table of contents directory or web portal. A brief review is given on existing methods for document clustering and organization, and clustering validation measures. The proposed approach has been tested and compared with several existing methods on real world web page datasets. The results have clearly demonstrated the advantages and efficiency in content organization of the proposed method in terms of computational cost and representation. The TOC can be easily adapted for large-scale applications. The topology provides a unique, additional feature for retrieving related topics and confining the search space.