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We present a way of exploiting domain knowledge in the design and implementation of data mining algorithms, with special attention to frequent patterns discovery, within a deductive framework. In our framework, domain knowledge is represented by way of deductive rules, and data mining algorithms are specified by means of iterative user-defined aggregates and implemented by means of user-defined predicates. This choice allows us to exploit the full expressive power of deductive rules without loosing in performance. Iterative user-defined aggregates have a fixed scheme, in which user-defined predicates are to be added. This feature allows the modularization of data mining algorithms, thus providing a way to integrate the proper domain knowledge exploitation in the right point. As a case study, the paper presents how user-defined aggregates can be exploited to specify and implement a version of the a priori algorithm. Some performance analyzes and comparisons are discussed in order to show the effectiveness of the approach.
Data mining, query languages, constraint and logic languages, rule-based databases, user-defined aggregates, association rules.
Franco Turini, Giuseppe Manco, Fosca Giannotti, "Specifying Mining Algorithms with Iterative User-Defined Aggregates", IEEE Transactions on Knowledge & Data Engineering, vol. 16, no. , pp. 1232-1246, October 2004, doi:10.1109/TKDE.2004.64
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