Clustering and classification in structured data domains using Fuzzy Lattice Neurocomputing (FLN)

A connectionist scheme, namely, sigma -Fuzzy Lattice Neurocomputing scheme or sigma -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 sigma -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 sigma -FLN in order to compute clusters of semantically related words, namely, hyperwords. The arithmetic parameters of sigma -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.