Efficiently mining long patterns from databases
- 1 June 1998
- journal article
- Published by Association for Computing Machinery (ACM) in ACM SIGMOD Record
- Vol. 27 (2) , 85-93
- https://doi.org/10.1145/276305.276313
Abstract
We present a pattern-mining algorithm that scales roughly linearly in the number of maximal patterns embedded in a database irrespective of the length of the longest pattern. In comparison, previous algorithms based on Apriori scale exponentially with longest pattern length. Experiments on real data show that when the patterns are long, our algorithm is more efficient by an order of magnitude or more.Keywords
This publication has 5 references indexed in Scilit:
- Dynamic itemset counting and implication rules for market basket dataPublished by Association for Computing Machinery (ACM) ,1997
- An effective hash-based algorithm for mining association rulesPublished by Association for Computing Machinery (ACM) ,1995
- Mining association rules between sets of items in large databasesPublished by Association for Computing Machinery (ACM) ,1993
- An information theoretic approach to rule induction from databasesIEEE Transactions on Knowledge and Data Engineering, 1992
- A New Algorithm for Generating Prime ImplicantsIEEE Transactions on Computers, 1970