In recent years, there has been considerable interest in the analysis of social annotations. Social annotations allow users to annotate web resources more easily, openly and freely than do taxonomies and ontologies. In this paper, we propose a novel algorithm for social annotations. It introduces a fuzzy biclustering algorithm to social annotations for identifying subgroups of users and of resources, and discovering the relationships between those users for social annotations. The algorithm employs a combination of pattern search and compromise programming to construct hierarchically structured biclusters. The pattern search method is used to compute a single objective optimal solution, and the compromise programming is used to trade-off between multiple objectives. The algorithm is not subject to the convexity limitations, and does not need to use the derivative information. It can automatically identify user communities and achieve high prediction accuracies.
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