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基于线性链表的模糊关联规则挖掘 被引量：6

Linear Linklist Based Algorithm for Fuzzy Association Rule Mining
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摘要 为改进现有模糊关联规则挖掘算法的不足,提出了一种基于线性链表的模糊关联规则挖掘算法。算法利用线性链表只存储有用的事务数据库信息,并不断利用前期的运算结果对之进行简化,减少了数据的存储开销及扫描时间,降低了算法的时间复杂度,提高了算法的效率。比较分析以及实验表明,该算法对于挖掘模糊关联规则是快速而有效的。 In order to improve the efficiency of existing fuzzy association rule mining algorithms, we presented a linear linklist based algorithm for fuzzy association rule mining. Utilizing the linear linklist our algorithm only records the information of the tran-sactions which are useful for counting the support of the frequent itemset, and simplifies the transactions information according to the previous results, which reduces the cost of data storage and increases the running efficiency. Experiments demonstrate that our method is efficient in fuzzy association rule mining.
作者 刘青宝 王文熙 王万军
机构地区 国防科学技术大学信息系统工程重点实验室
出处 《计算机科学》 CSCD 北大核心 2012年第3期135-138,共4页 Computer Science
基金 国家自然科学基金(70771110)资助
关键词 数据挖掘 模糊关联规则 线性链表 Data mining, Fuzzy association rule, Linear linklist
分类号 TP311.13 [自动化与计算机技术—计算机软件与理论]
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参考文献7

  • 1Chan K C C,Au W-H. Mining fuzzy association rules [C]//Proceedings of the 6th ACM International Conference on Information and Knowledge Management. Las Vegas. Nevada, USA, 1997:209-215.
  • 2Hong T P, Kuo C S, Chi S C. Mining association rules from quantitative data[J].Intelligent Data Analysis, 1999, 3: 363-376.
  • 3Hong T P,Kuo C S,Wang S L. A fuzzy AprioriTid mining algorithm with reduced computational time[J]. Applied Soft Computing, 2004,5 : 1-10.
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  • 5Papadimitriou S, Mavroudi S. The frequent fuzzy pattern tree [C] // Proceeding of the 9th WSEAS International Conference on Computers. 2005.
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同被引文献58

  • 1Srivastava J, Cooleyr, Deshpande M. Web usage mining: discovery and application of usage patterns from Web data. IGKDD Explora- tions, 2000 ; 22( 1) : 34^40.
  • 2Gong Maoguo, Jiao Licheng, Liu Fang, et al. immune algorithm with orthogonal design based initialization,cloning,and selection for global optimization. Knowledge and Information Systems, 2010; 25 (3 ):523-549.
  • 3Yang Dongdong, Jiao Licheng, Gong Maoguo, et al. Adaptiveranks and K-nearest neighbour list based multiobjective immune algo-rithm. Computational Intelligence, 2010;26(4) :359-385.
  • 4Agrawal R,Lmielinski T,Swami A. Mining association rules between sets of items in large databases[C]//The ACM SIGMOD Int Conf on Manage- ment of Data. Washington: ACM SIGMOD, 1993 : 207-216.
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  • 6Hong T P,Kuo C S,Chi S C. A fuzzy data mining algorithm for quantitative values[C]//The Third International Conference on Knowledge-Based Intelligent Information Engineeing Systems. Adelaide : Adelaide University, 1999 : 480-483.
  • 7Lin C W,Hong T P,Lu W H. Linguistic data mining with fuzzy FP-trees[J]. Expert Systems with Applications,2010,37:4560-4567.
  • 8Hang T P,Kuo C S,Chi S C. Trade-off between time complexity and number of rules for fuzzy mining from quantitative data[J]. International Journal of Uncertainty Fuzziness and Knowledge-BasedSystems ,2001,9(5) :587-604.
  • 9Cao L, Zhao Y, Zhang H, et al. Flexible frameworks for actionable knowledge discovery. IEEE Transactions on Knowledge and Data En- gineering, 2010 ;22 ( 9 ) : 1299-1312.
  • 10Chiu H P, Tang Y T,Hsieh K L. Applying cluster-based fuzzy asso- ciation rules mining framework into EC environment. Apptied Soft Computing, 2012;12 (8) : 2114-2122.

引证文献6

二级引证文献15

计算机科学
2012年 第3期

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