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- W2167108874 abstract "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, we present 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." @default.
- W2167108874 created "2016-06-24" @default.
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- W2167108874 date "2004-10-01" @default.
- W2167108874 modified "2023-10-18" @default.
- W2167108874 title "Specifying mining algorithms with iterative user-defined aggregates" @default.
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- W2167108874 doi "https://doi.org/10.1109/tkde.2004.64" @default.
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