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Specifying mining algorithms with iterative user-defined aggregates

delete2004-10-01
delete10
PRE
AI
F
Fosca Giannotti
G
Giuseppe Manco
F
Franco Turini
DOI：10.1109/TKDE.2004.64delete
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Abstract

Abstract

En 中文
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.
Keywords:
data mining
query languages
constraint and logic languages
rule-based databases
user-defined aggregates
association rules
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Journal

IEEE Transactions on Knowledge and Data Engineering cover
IEEE Transactions on Knowledge and Data Engineering
IF:
10.4
Papers:
6.7K
Citations:
3.2W

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