A SMOOTH INTRODUCTION TO SYMBOLIC METHODS FOR KNOWLEDGE DISCOVERY

In this chapter, we present a smooth introduction to symbolic methods for knowledge discovery in databases (KDD). The KDD process extracts from large databases information units that can be interpreted as knowledge units to be reused. This process has three major steps: the selection and preparation of data, the data mining operation, and finally the interpretation of the extracted units. The process may take advantage of domain knowledge embedded in domain ontologies, which may be used at every step of the KDD process. We describe three symbolic methods for KDD: lattice-based classification, frequent itemset search, and association rule extraction. Then, we present three applications of the KDD process, and end the chapter with a discussion of the main characteristics of KDD.

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