Data Mining Query Languages

Abstract

Summary. Many Data Mining algorithms enable to extract different types of patterns from data (e.g., local patterns like itemsets and association rules, models like classifiers). To support the whole knowledge discovery process, we need for integrated systems which can deal either with patterns and data. The inductive database approach has emerged as an unifying framework for such systems. Following this database perspective, knowledge discovery processes become querying processes for which query languages have to be designed. In the prolific field of association rule mining, different proposals of query languages have been made to support the more or less declarative specification of both data and pattern manipulations. In this chapter, we survey some of these proposals. It enables to identify nowadays shortcomings and to point out some promising directions of research in this area.Key words: Query languages, Association Rules, Inductive Databases. The Need for Data Mining Query LanguagesSince the first definition of the Knowledge Discovery in Databases (KDD) domain in (Piatetsky-Shapiro and Frawley, 1991), many techniques have been proposed to support these "From Data to Knowledge" complex interactive and iterative processes. In practice, knowledge elicitation is based on some extracted and materialized (collections of) patterns which can be global (e.g., decision trees) or local (e.g., itemsets, association rules). Real life KDD processes imply complex pre-processing manipulations (e.g., to clean the data), several extraction steps with different parameters and types of patterns (e.g., feature construction by means of constrained itemsets followed by a classifying phase, association rule mining for different thresholds values and different objective measures of interestingness), and post-processing manipulations (e.g., elimination of redundancy in extracted patterns, crossing-over operations between patterns and data like the search of transactions which are exceptions to frequent and valid association rules or the selection of misclassified examples with a decision tree). Looking for a tighter integration between data and patterns which hold in the data, Imielinski and Mannila have proposed in (Imielinski and Mannila, 1996) the concept of inductive database (IDB). In an IDB, ordinary queries can be used to access and manipulate data, while inductive queries can be used to generate (mine), manipulate, and apply patterns. KDD becomes O. Maimon, L. Rokach (eds.), Data Mining and Knowledge Discovery Handbook, 2nd ed.,

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