计算机科学 ›› 2015, Vol. 42 ›› Issue (9): 41-44.doi: 10.11896/j.issn.1002-137X.2015.09.009
崔亮,郭静,吴玲达
CUI Liang, GUO Jing and WU Ling-da
摘要： 关联规则挖掘搜索给定数据集中反复出现的数据模式,找到它们之间的相关性。分析了经典Apriori算法存在的时空效率低的缺点和数据形式对算法效率的影响。提出一种基于动态散列和事务压缩技术的改进,动态应用散列技术减小候选频繁项集的规模和数据库扫描次数,应用事务压缩技术缩小数据库中事务量的长度和总数,从而提高了算法的时间空间效率。与Apriori算法进行的比较验证了新算法的正确性与效率。
| [1] Agrawal R,Srikant R.Fast algorithms for mining association rules[C]∥Proc.20th Int.Conf.Very Large Data Bases,VLDB.1994,1215:487-499 [2] 韩家炜,坎伯.数据挖掘:概念与技术[M].北京:机械工业出版社,2001:130-160 Han Jia-wei,Kamber M.Data Mining:Concepts and Techniques[M].Beijing:China Machine Press,2001:130-160 [3] Yen S J,Wang C K,Ouyang L Y.A Search Space Reduced Algorithm for Mining Frequent Patterns[J].Journal of Information Science and Engineering,2012,28(1):177-191 [4] Park J S,Chen M S,Yu P S.An effective hash-based algorithm for mining association rules[J].ACM SIGMOD Record,1995,24(2):175-186 [5] Singh J,Ram H,Sodhi D J S.Improving Efficiency of Apriori Algorithm Using Transaction Reduction[J].International Journal of Scientific and Research Publications,2013,3(1):1-4 [6] Savasere A,Omiecinski E R,Navathe S B.An efficient algorithm for mining association rules in large databases[J].ACM SIGMOD Record,2000,9(2):1-12 [7] Toivonen H.Sampling large databases for association rules[C]∥VLDB.1996,96:134-145 [8] Brin S,Motwani R,Ullman J D,et al.Dynamic itemset counting and implication rules for market basket data[J].ACM SIGMOD Record.ACM,1997,6(2):255-264 [9] 孟军,王蓬,张静,等.基于项集依赖的最小关联规则挖掘[J].计算机科学,2013,40(1):183-186,217 Meng Jun,Wang Peng,Zhang Jing,et al.Minimal Association Rules Mining Based on Itemset Dependency[J].Computer Scie-nce,2013,0(1):183-186,7 |
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