Fuzzy lattice reasoning (FLR) type neural computation for weighted graph partitioning

TitleFuzzy lattice reasoning (FLR) type neural computation for weighted graph partitioning
Publication TypeJournal Article
Year of Publication2009
AuthorsKaburlasos, Vassilis G., Lefteris Moussiades, and Athena Vakali
JournalNeurocomputing
Volume72
Pagination2121-2133
KeywordsClustering, Fuzzy lattices, Graph partitioning, Metric Measurable path, Similarity measure
Abstract

The fuzzy lattice reasoning (FLR) neural network was introduced lately based on an inclusion measurefunction. This work presents a novel FLR extension, namely agglomerative similarity measure FLR, orasmFLR for short, for clustering based on a similarity measure function, the latter (function) may also bebased on a metric. We demonstrate application in a metric space emerging from a weighted graphtowards partitioning it. The asmFLR compares favorably with four alternative graph-clusteringalgorithms from the literature in a series of computational experiments on artificial data. In addition,our work introduces a novel index for the quality of clustering, which (index) compares favorably withtwo popular indices from the literature.

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