Computational organization theory
Computational organization theory
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Efficient identification of Web communities
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Techniques of Cluster Algorithms in Data Mining
Data Mining and Knowledge Discovery
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Calling communities analysis and identification using machine learning techniques
Expert Systems with Applications: An International Journal
Cancer class prediction: Two stage clustering approach to identify informative genes
Intelligent Data Analysis
ADVIS'04 Proceedings of the Third international conference on Advances in Information Systems
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Clustering by integrating multi-objective optimization with weighted k-means and validity analysis
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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In this paper, we consider genes as actors of a social network, a research area that has not yet received attention in the literature of social network mining and analysis. Even though our research project covers both genes and proteins, we concentrate in this paper on gene; we first try to describe the gene expression data and how gene interactions can be realised as a social network. Then we describe how data mining techniques could reveal important information by identifying disease biomarkers from the social communities of genes. This is possible because of the way genes interact and form communities that are anticipated to have certain effects on the different processes that take place within an organism. Gene communities both contribute to the development of an organism by coding proteins and cause serious diseases. In this paper, we concentrate on genes that act as cancer biomarkers. We apply a multiobjective clustering approach to produce alternative clustering solutions and then derive a matrix that reflects the link between genes based on their common occurrence on the same cluster within different alternative solutions. The latter matrix leads to the social network of genes, which is then analysed to discover the communities and the central genes within each community. The latter genes are studied further as cancer biomarkers. The test results are promising in demonstrating the applicability and effectiveness of the developed mining-based methodology.