Mine Rule

Mining of association rules is one of the most adopted techniques for data mining in the most widespread application domains. A great deal of work has been carried out in the last years on the development of efficient algorithms for association rules extraction. Indeed, this problem is a computationally difficult task, known as NP-hard (Calders, 2004), which has been augmented by the fact that normally association rules are being extracted from very large databases. Moreover, in order to increase the relevance and interestingness of obtained results and to reduce the volume of the overall result, constraints on association rules are introduced and must be evaluated (Ng et al.,1998; Srikant et al., 1997). However, in this contribution, we do not focus on the problem of developing efficient algorithms but on the semantic problem behind the extraction of association rules (see Tsur et al. [1998] for an interesting generalization of this problem). We want to put in evidence the semantic dimensions that characterize the extraction of association rules; that is, we describe in a more general way the classes of problems that association rules solve. In order to accomplish this, we adopt a general-purpose query language designed for the extraction of association rules from relational databases. The operator of this language, MINE RULE, allows the expression of constraints, constituted by standard SQL predicates that make it suitable to be employed with success in many diverse application problems. For a comparison between this query language and other state-of-the-art languages for data mining, see Imielinski, et al. (1996); Han, et al. (1996); Netz, et al. (2001); Botta, et al. (2004). In Imielinski, et al. (1996), a new approach to data mining is proposed, which is constituted by a new generation of databases called Inductive Databases (IDBs). With an IDB, the user/analyst can use advanced query languages for data mining in order to interact with the knowledge discovery (KDD) system, extract data mining descriptive and predictive patterns from the database, and store them in the database. Boulicaut, et al. (1998) and Baralis, et al. (1999) discuss the usage of MINE RULE in this context. We want to show that, thanks to a highly expressive query language, it is possible to exploit all the semantic possibilities of association rules and to solve very different problems with a unique language, whose statements are instantiated along the different semantic dimensions of the same application domain. We discuss examples of statements solving problems in different application domains that nowadays are of a great importance. The first application is the analysis of a retail data, whose aim is market basket analysis (Agrawal et al., 1993) and the discovery of user profiles for customer relationship management (CRM). The second application is the analysis of data registered in a Web server on the accesses to Web sites by users. Cooley, et al. (2000) present a study on the same application domain. The last domain is the analysis of genomic databases containing data on micro-array experiments (Fayyad, 2003). We show many practical examples of MINE RULE statements and discuss the application problems that can be solved by analyzing the association rules that result from those statements.