Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Graph-Based Evolutionary Approach to Sequence Clustering
ICMLA '05 Proceedings of the Fourth International Conference on Machine Learning and Applications
On semi-supervised clustering via multiobjective optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Graph evolution: Densification and shrinking diameters
ACM Transactions on Knowledge Discovery from Data (TKDD)
Multiobjective clustering with automatic k-determination for large-scale data
Proceedings of the 9th annual conference on Genetic and evolutionary computation
ACM Transactions on Knowledge Discovery from Data (TKDD)
Genetic-guided semi-supervised clustering algorithm with instance-level constraints
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A multi-stack based phylogenetic tree building method
ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
An Evolutionary Approach to Multiobjective Clustering
IEEE Transactions on Evolutionary Computation
A novel similarity-based modularity function for graph partitioning
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Multiobjective evolutionary algorithms for dynamic social network clustering
Proceedings of the 12th annual conference on Genetic and evolutionary computation
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Mesoscopic analysis of networks with genetic algorithms
World Wide Web
Data Mining and Knowledge Discovery
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Size and complexity of data repositories collaboratively created by Web users generate a need for new processing approaches. In this paper, we study the problem of detection of fine-grained communities of users in social networks, which can be defined as clustering with a large number of clusters. The practical size of social networks makes the traditional evolutionary based clustering approaches, which represent the entire clustering solution as one individual, hard to apply. We propose an Agglomerative Clustering Genetic Algorithm (ACGA): a population of clusters evolves from the initial state in which each cluster represents one user to a high quality clustering solution. Each step of the evolutionary process is performed locally, engaging only a small part of the social network limited to two clusters and their direct neighborhood. This makes the algorithm practically useful independently of the size of the network. Evaluation on two social network models indicates that ACGA is potentially able to detect communities with accuracy comparable or better than two typical centralized clustering algorithms even though ACGA works under much stricter conditions.