A quality driven Hierarchical Data Divisive Soft Clustering for information retrieval
Knowledge-Based Systems
Fusion of knowledge towards the identification of genetic profiles
AI Communications
Neural networks to predict earthquakes in Chile
Applied Soft Computing
A multiobjective evolutionary programming framework for graph-based data mining
Information Sciences: an International Journal
Three-objective subgraph mining using multiobjective evolutionary programming
Journal of Computer and System Sciences
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Current tools and techniques devoted to examine the content of large databases are often hampered by their inability to support searches based on criteria that are meaningful to their users. These shortcomings are particularly evident in data banks storing representations of structural data such as biological networks. Conceptual clustering techniques have demonstrated to be appropriate for uncovering relationships between features that characterize objects in structural data. However, typical conceptual clustering approaches normally recover the most obvious relations, but fail to discover the less frequent but more informative underlying data associations. The combination of evolutionary algorithms with multiobjective and multimodal optimization techniques constitutes a suitable tool for solving this problem. We propose a novel conceptual clustering methodology termed evolutionary multiobjective conceptual clustering (EMO-CC), relying on the NSGA-II multiobjective (MO) genetic algorithm. We apply this methodology to identify conceptual models in structural databases generated from gene ontologies. These models can explain and predict phenotypes in the immunoinflammatory response problem, similar to those provided by gene expression or other genetic markers. The analysis of these results reveals that our approach uncovers cohesive clusters, even those comprising a small number of observations explained by several features, which allows describing objects and their interactions from different perspectives and at different levels of detail.