Automatic structuring and retrieval of large text files
Communications of the ACM
Hypertext-like structures through a SOM network
Proceedings of the tenth ACM Conference on Hypertext and hypermedia : returning to our diverse roots: returning to our diverse roots
The LBG-U Method for Vector Quantization – an Improvement over LBGInspired from Neural Networks
Neural Processing Letters
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Document clustering based on semantics is a fundamental method of helping users to search and browse in large collections of documents. Recently a number of papers have reported the applications of self-organizing artificial neural networks in document clustering based on semantics. In particular Growing Neural Gas is a growing neural network that allows the user to reproduce the topological distribution of the inputs, but the structure obtained often has the same complexity as the input data structure; if the input space has more than three dimensions it is impossible to visualize or represent the GNG network as well as the input data structure. In this paper the authors propose a LBG modified network, called LBG-m, that can simplify the GNG structure in order to visualize and summarize it. The two algorithms constitute a tool for browsing large document sets and generating a set of semantic links between clusters of similar documents.