ABSTRACT
Association rules are interesting correlations among
attributes in a database. These rules have many applications in
areas ranging from e-commerce to sports to census analysis to
medical diagnosis. The discovery of association rules is an
extremely computationally expensive task and it is therefore
imperative to have fast scalable algorithms for mining these
rules. Performing Existing association mining algorithms
requires repeated passes over the entire database. Obviously, for
large database, the role of input/output overhead in scanning the
database is very significant. In this paper we have proposed a
new way of data mining OTM (One Time Mining) in which we
mine the dataset only once and we can use this result to obtain
result for any user specified support value without further
mining dataset. To perform OTM we have used RCS (Reduced
Candidate Set) algorithm for association mining.
Keywords: Association mining, Data mining, CPU and I/O overhead, Large size database, OTM, RCS algorithm.
