@article {journals/ijon/KaburlasosMV09, title = {Fuzzy lattice reasoning (FLR) type neural computation for weighted graph partitioning}, journal = {Neurocomputing}, volume = {72}, number = {10-12}, year = {2009}, pages = {2121-2133}, 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.

}, keywords = {Clustering, Fuzzy lattices, Graph partitioning, Metric Measurable path, Similarity measure}, author = {Kaburlasos, Vassilis G. and Moussiades, Lefteris and Athena Vakali} }