Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Selecting and reporting what is interesting
Advances in knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
An improved data mining approach using predictive itemsets
Expert Systems with Applications: An International Journal
An empirical study on mining sequential patterns in a grid computing environment
Expert Systems with Applications: An International Journal
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A central part of many algorithms for mining association rules in large data sets is a procedure that finds so called frequent itemsets. This paper proposes a new approach to finding frequent itemsets. The approach reduces a number of passes through an input data set and generalises a number of strategies proposed so far. The idea is to analyse a variable number n of itemset lattice levels in p scans through an input data set. It is shown that for certain values of parameters (n,p) this method provides more flexible utilisation of fast access transient memory and faster elimination of itemsets with low support factor. The paper presents the results of experiments conducted to find how performance of association rule mining algorithm depends on the values of parameters (n,p).