关键词: 数据挖掘, 关联分析, 频繁项集

Abstract: Association analysis techniques in data mining are aimed at discovering interesting association among a large number of data itemsets，and the core problem is to find frequent itemsets.In traditional association mining algorithm based on matrix，the matrix size is related to the transaction database size.Thus memory bottlenecks still exist in dealing with very large transaction databases.This paper presents a frequent itemsets discovery algorithm to solve this problem.In the pre-mining and post-validating matrix constrained algorithm the matrix size is independent of the transaction database size.Experimental results show that this algorithm improves the speed of frequent itemsets mining.

Key words: data mining, association analysis, frequent itemsets