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

被引:35
|
作者
Petridis, V [1 ]
Kaburlasos, VG [1 ]
机构
[1] Aristotelian Univ Salonika, Dept Elect & Comp Engn, GR-54006 Salonika, Greece
关键词
text classification; neural networks; clustering; graphs; framework of fuzzy lattices;
D O I
10.1109/69.917564
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:245 / 260
页数:16
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