文档介绍：DATA &
KNOWLEDGE
ENGINEERING
ELSEVIER Data & Knowledge Engineering 17 (1995) 245-262
Set-oriented data mining in relational databases
Maurice Houtsma"'*, Arun Swami b't
"University of Twente, Enschede, The Netherlands
blBM Almaden Research Center, San Jose, CA, USA
Received 25 July 1994; revised 14 March 1995; accepted 28 July 1995
Abstract
Data mining is an important real-life application for businesses. It is critical to find efficient ways of mining large
data sets. In order to benefit from the experience with relational databases, a set-oriented approach to mining data
is needed. In such an approach, the data mining operations are expressed in terms of relational or set-oriented
operations. Query optimization technology can then be used for efficient processing.
In this paper, we describe set-oriented algorithms for mining association rules. Such algorithms imply performing
multiple joins and thus may appear to be inherently less efficient than special-purpose algorithms. We develop new
algorithms that can be expressed as SQL queries, and discuss optimization of these algorithms. After analytical
evaluation, an algorithm named SETM emerges as the algorithm of choice. Algorithm SETM uses only simple
database primitives, viz., sorting and merge-scan join. Algorithm SETM is simple, fast, and stable over the range of
parameter values. It is easily parallelized and we suggest several additional optimizations. The set-oriented nature
of Algorithm SETM makes it possible to develop extensions easily and its performance makes it feasible to build
interactive data mining tools for large databases.