Multi-Relational Data Mining (1999)
| Citations: | 15 - 0 self |
BibTeX
@TECHREPORT{Knobbe99multi-relationaldata,
author = {Arno J. Knobbe and H. Blockeel and A.P.J.M. Siebes and D.M.G. van der Wallen and D. M. G. Van Der and Mathematisch Centrum (smc and The Dutch Foundation and Arno J. Knobbe and Hendrik Blockeel and Arno Siebes},
title = {Multi-Relational Data Mining},
institution = {},
year = {1999}
}
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Abstract
An important aspect of data mining algorithms and systems is that they should scale well to large databases. A consequence of this is that most data mining tools are based on machine learning algorithms that work on data in attribute-value format. Experience has proven that such 'single-table' mining algorithms indeed scale well. The downside of this format is, however, that more complex patterns are simply not expressible in this format and, thus, cannot be discovered. One way to enlarge the expressiveness is to generalize, as in ILP, from onetable mining to multiple table mining, i.e., to support mining on full relational databases. The key step in such a generalization is to ensure that the search space does not explode and that efficiency and, thus, scalability are maintained. In this paper we present a framework and an architecture that provide such a generalization. In this framework the semantic information in the database schema, e.g., foreign keys, are exploited ...
