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Clustering and Classification in Structured Data Domains Using Fuzzy Lattice Neurocomputing (FLN)
March/April 2001 (vol. 13 no. 2)
pp. 245-260

Abstract—A connectionist scheme, namely, σ-Fuzzy Lattice Neurocomputing scheme or σ-FLN for short, which has been introduced in the literature lately for clustering in a lattice data domain, is employed in this work for computing clusters of directed graphs in a master-graph. New tools are presented and used here, including a convenient inclusion measure function for clustering graphs. A directed graph is treated by σ-FLN as a single datum in the mathematical lattice of subgraphs stemming from a master-graph. A series of experiments is detailed where the master-graph emanates from a Thesaurus of spoken language synonyms. The words of the Thesaurus are fed to σ-FLN in order to compute clusters of semantically related words, namely, hyperwords. The arithmetic parameters of σ-FLN can be adjusted so as to calibrate the total number of hyperwords computed in a specific application. It is demonstrated how the employment of hyperwords implies a reduction, based on the a priori knowledge of semantics contained in the Thesaurus, in the number of features to be used for document classification. In a series of comparative experiments for document classification, it appears that the proposed method favorably improves classification accuracy in problems involving longer documents, whereas performance deteriorates in problems involving short documents.

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Index Terms:
Text classification, neural networks, clustering, graphs, framework of fuzzy lattices.
Vassilios Petridis, Vassilis G. Kaburlasos, "Clustering and Classification in Structured Data Domains Using Fuzzy Lattice Neurocomputing (FLN)," IEEE Transactions on Knowledge and Data Engineering, vol. 13, no. 2, pp. 245-260, March-April 2001, doi:10.1109/69.917564
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