|Title||Fuzzy lattice reasoning (FLR) type neural computation for weighted graph partitioning|
|Publication Type||Journal Article|
|Year of Publication||2009|
|Authors||Kaburlasos, Vassilis G., Lefteris Moussiades, and Athena Vakali|
|Keywords||Clustering, Fuzzy lattices, Graph partitioning, Metric Measurable path, Similarity measure|
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.
Fuzzy lattice reasoning (FLR) type neural computation for weighted graph partitioning