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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.
Citation:
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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