Clustering and classification in structured data domains using Fuzzy Lattice Neurocomputing (FLN)
V. Petridis, Vassilis G. Kaburlasos · IEEE Transactions on Knowledge and Data Engineering · 2001
A connectionist scheme, namely, /spl sigma/-Fuzzy Lattice Neurocomputing scheme or /spl sigma/-FLN for short, which has been introduced in the literature lately for clustering in a lattice data domain, is employed for computing clusters of directed graphs in a master-graph. New tools are presented and used, including a convenient inclusion measure function for clustering graphs. A directed graph is treated by /spl 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 /spl sigma/-FLN in order to compute clusters of semantically related words, namely hyperwords. The arithmetic parameters of /spl 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.