A clustering framework for the process of knowledge discovery in databases

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Knowledge Discovery in Databases (KDD) has been defined as the process of extracting valid, novel, understandable and potentially useful patterns from large databases. The KDD process involves several steps, in particular focusing, pre-processing, data mining and evaluation, that normally have to be iterated to achieve satisfactory results. So far, most KDD research has focused on the data mining step, developing efficient algorithms for tasks such as clustering, classification and association rule mining. Unfortunately, not much research has addressed the other steps and the process as a whole, which has seriously limited the usefulness of existing data mining methods. In this project, we want to explore support for the entire KDD process in the context of clustering, one of the most important data mining tasks. The lack of support for all KDD steps is especially problematic for clustering due to its unsupervised, exploratory nature and because most clustering algorithms do not generate explicit patterns, but return clusters simply as sets of objects. The objective of this proposed project is to develop a framework for clustering supporting all KDD steps. Most existing clustering algorithms exploit only attributes of the objects to be clustered, but in many emerging applications relationships among the target table and attributes from related tables play an important role in representing the objects of interest. In market segmentation, e.g., not only the purchasing preferences but also the social network among the customers is relevant for clustering. When clustering gene expression data, as another example, attributes of the related proteins and their further relationships must be considered. We plan to evaluate our clustering framework in close collaboration with domain experts in the applications of analysis of gene expression data, analysis of flow cytometry data as well as community identification and market segmentation.