A minimal perfect hashing scheme to mining association rules from frequently updated data

Abstract

Data mining techniques have attracted the attention of researchers from various areas. One of the most important issues in data mining is the mining of association rules from very large and frequently updated databases. In this paper, an algorithm for mining association rules based on a minimal perfect hashing scheme is presented. By generating non‐collision hashing tables, the novel approach is especially suitable for handling very large databases containing huge amounts of transactions and frequently updated data. Some experiments have been performed on the databases with the number of transactions ranging from 10,000 to 1,000,000. The experimental result shows that our novel approach performs much better than previously proposed methods.