The Role of Granular Information in Knowledge Discovery in Databases

In this study, we are concerned with the role of information granulation in processes of data mining in databases. By its nature, data mining pursuits are very much oriented towards end-users and imply that any results need to be easily interpretable. Granulation of information promotes this interpretability and channels all pursuits of data mining (that are otherwise computationally intensive and thus highly prohibitive) towards more efficient processing and feasible processing of information granules. First, we discuss the essence of information granulation and afterwards elaborate on the main approaches to the design of information granules. We distinguish between user-driven, data-driven and hybrid methods of information granulation. Several main classes of membership function of information granules — fuzzy sets are investigated and contrasted in terms of some selection criteria such as parametric flexibility and sensitivity of the ensuing information granules. We revisit two fundamental concep ts in data mining such as associations and rules in the setting of information granules. Associations are direction-free constructs that capture the most essential components of the overall structure in database. The relevance of associations is expressed by counting the amount of data standing behind the Cartesian products of the information granules contributing to the construction of the associations. The proposed methodology of data mining comprises two phases. First, associations are constructed and the most essential (relevant) ones are collected in the form of a data mining agenda. Second, some of them are converted into direction-driven constructs, that is rules. The idea of consistency of the rules is discussed in detail.