Please use this identifier to cite or link to this item:
|Title:||Clustering algorithm based on asymmetric similarity and paradigmatic features|
|Authors:||Santisteban Pablo, Julio Omar|
Tejada Cárcamo, Javier
|Keywords:||Classification (of information);Cluster analysis;Graph theory;Pattern recognition;Semantics;Asymmetric similarity;Jaccard similarity coefficients;Paradigmatic similarity;Pattern recognition problems;Semantic relationships;Similarity;Synthetic and real data;Traditional approaches;Clustering algorithms|
|Publisher:||Inderscience Enterprises Ltd.|
|Abstract:||Similarity measures are essential to solve many pattern recognition problems such as classification, clustering, and information retrieval. Various similarity measures are categorised in both syntactic and semantic relationships. In this paper, we present a novel similarity, unilateral Jaccard similarity coefficient (uJaccard), which does not only take into consideration the space among two points but also the semantics among them. How can we retrieve meaningful information from a large and sparse graph? Traditional approaches focus on generic clustering techniques for network graph. However, they tend to omit interesting patterns such as the paradigmatic relations. In this paper, we propose a novel graph clustering technique modelling the relations of a node using the paradigmatic analysis. Our proposed algorithm paradigmatic clustering (PaC) for graph clustering uses paradigmatic analysis supported by an asymmetric similarity using uJaccard. Extensive experiments and empirical analysis are used to evaluate our algorithm on synthetic and real data. Copyright © 2016 Inderscience Enterprises Ltd.|
|Appears in Collections:||Artículos de investigación|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.