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Abstract-Separating the noise from data in a clustering process is an important issue in practical applications. Various algorithms, most of them based on density functions approaches, have been developed lately. The aim of this work is to analyze the ability of an ant-based clustering algorithm (AntClust) to deal with noise. The basic idea of the approach is to extend the information carried by an ant with an information concerning the density of data in its neighborhood. Experiments on some synthetic test data suggest that this approach could ensure the separation of noise from data without significantly increasing the algorithm's complexity.

In this work, we propose an approach for evolving rules from medical data based on an interactive multi-criteria evolutionary search: besides selecting the set of criteria and the sets of potential antecedent and consequent attributes, the user can also intervene in the searching process by marking the uninteresting rules. The marked rules are further used in estimating a supplementary optimization criterion which expresses the user's opinion on the rule quality and is taken into account in the evolutionary process.