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 deductive rules, and data mining algorithms are constructed by means of iterative userdefined aggregates. Iterative user-defined aggregates have a fixed scheme that allows the modularization of data mining algorithms, thus providing a way to exploit domain knowledge in the right point. As a case study, the paper presents user-defined aggregates for specifying a version of the apriori algorithm. Some performance analyses and comparisons are discussed in order to show the effectiveness of the approach.
CITATION STYLE
Giannotti, F., Manco, G., & Turini, F. (2001). Specifying mining algorithms with iterative user-defined aggregates: A case study. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2168, pp. 128–139). Springer Verlag. https://doi.org/10.1007/3-540-44794-6_11
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