Irregular Community Discovery for Social CRM in Cloud Computing

Authors:
Jin Liu;Fei Liu;Jing Zhou;Chengwan He
Affiliations:
State Key Lab. Of Software Engineering, Wuhan University, China 430072 and State Key Lab. for Novel Software Technology, Nanjing University, China 210093;State Key Lab. Of Software Engineering, Wuhan University, China 430072;School of Computer, Communication University of China 100024;School of Computer Science and Engineering, Wuhan Institute of Technology 430073
Venue:
CloudCom '09 Proceedings of the 1st International Conference on Cloud Computing
Year:
2009

Citing 3
Cited 0

Enumerating all connected maximal common subgraphs in two graphs

Theoretical Computer Science
An algorithm for reporting maximal c-cliques

Theoretical Computer Science
Discovering Protein Complexes from Protein-Protein Interaction Data by Local Cluster Detecting Algorithm

FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 04

Quantified Score

Hi-index	0.00

Visualization

Abstract

Social CRM is critical in utilities services provided by cloud computing. These services rely on virtual customer communities forming spontaneously and evolving continuously. Thus clarifying the explicit boundaries of these communities is quite essential to the quality of utilities services in cloud computing. Communities with overlapping feature or projecting vertexes are usually typical irregular communities. Traditional community identification algorithms are limited in discovering irregular topological structures from a CR networks. These uneven shapes usually play a prominent role in finding prominent customer which is usually ignored in social CRM. A novel method of discovering irregular community based on density threshold and similarity degree. It finds and merges primitive maximal cliques from the first. Irregular features of overlapping and prominent sparse vertex are further considered. An empirical case and a method comparison test indicates its efficiency and feasibility.