|Table of Contents|

New frequent patterns mining algorithm

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2016年01期
Page:
29-
Research Field:
Publishing date:

Info

Title:
New frequent patterns mining algorithm
Author(s):
Ye Haiqin1Liao Li1Wang Yifeng2Zhang Ailing3
1.School of Computer Science and Technology,Zhoukou Normal University,Zhoukou 466001,China; 2.73658 Troops,PLA,Chuzhou 239421,China; 3.Automation Station,71352 Troops,PLA,Anyang 55000,China
Keywords:
frequent patterns mining algorithms bit strings support typical sets transaction sets
PACS:
TP311
DOI:
-
Abstract:
In order to adapt to the frequent changes of the database by adding,deleting or modifying operations and speeding up the solving process of support,this paper proposes a new frequent patterns mining algorithm.To adapt to the frequent changes in the current database,customers’ once purchase behavior is converted into a bit string and the typical set of transaction sets is updated gradually by the operation on bit strings in this paper.The typical set includes all patterns.Frequent patterns can be found quickly from the typical set according to the support threshold.An example is used to analyse the process of the algorithm in the face of the frequent change database.It shows that the algorithm has strong ability of adapting to changes in the database and can find the frequent patterns quickly according to the given support threshold.Simulation results verify the effectiveness and feasibility of the algorithm.

References:

[1] Agrawal R,Imielinaki T,Swami A.Mining association rules between sets of items in large databases[C]//Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data.Washington D C,USA:ACM Press,1993:207-216.
[2]Yuan M,Ouyang Y X,Xiong Z.A text categorization method using extended vector space model by frequent term sets[J].Journal of Information Science and Engineering,2013,29(1):99-114.
[3]陈晓云,陈袆,王雷,等.基于分类规则树的频繁模式文本分类[J].软件学报,2006,17(5):1017-1025.

Chen Xiaoyun,Chen Yi,Wang Lei,et al.Text categorization based on classification rules tree by frequent patterns[J].Journal of Software,2006,17(5):1017-1025.
[4]Kim H D,Park D H,Lu Y,et al.Enriching text representation with frequent pattern mining for probabilistic topic modeling[J].Proceedings of the American Society for Information Science and Technology,2012,49(1):1-10.
[5]张辉,徐新文,李国辉.新闻事件多维频繁模式挖掘——基于多维属性索引树方法[J].系统工程,2014,32(3):143-148.
Zhang Hui,Xu Xinwen,Li Guohui.Mining multi-dimensional frequent pattern for news event based on multi-dimensional attribute index tree[J].Systems Engineering,2014,32(3):143-148.
[6]陈辉.一种基于位图计算并行挖掘大数据频繁模式算法[J].小型微型计算机系统,2014,35(7):1599-1603.Chen Hui.Parallel mining frequent patterns in big data based on bitmap computation[J].Journal of Chinese Computer Systems,2014,35(7):1599-1603.
[7]郑麟.一种直接生成频繁项集的分治Apriori算法[J].计算机应用与软件,2014,31(4):297-301.Zheng Lin.A divied-and-conquer Apriori algorithm directly generating frequent itemsets[J].Computer Applications and Software,2014,31(4):297-301.
[8]Malpani K,Pal P R.An efficient algorithms for generating frequent pattern using logical table with AND,OR operation[J].Computer Science and Telecommunications,2013,37(1):24-30.
[9]胡绿慧,任玉兰,何振林.基于划分和压缩数据库的改进Apriori算法[J].成都理工大学学报(自然科学版),2015,42(1):110-114.
Hu Lühui,Ren Yulan,He Zhenlin.Improved Apriori algorithm based on classification and database compression[J].Journal of Chengdu University of Technology(Science & Technology Edition),2015,42(1):110-114.
[10]刘双跃,杨蕾,彭丽.基于改进Apriori算法的煤矿物态隐患系统设计与应用[J].煤炭技术,2015,34(4):318-320.
Liu Shuangyue,Yang Lei,Peng Li.Design and application of coal mine state hidden danger system based on data mining[J].Coal Technology,2015,34(4):318-320.
[11]崔旭,刘小丽.基于粗糙集的改进Apriori算法研究[J].计算机仿真,2013,30(1):329-332.Cui Xu,Liu Xiaoli.Improved Apriori algorithm based on rough set[J].Computer Simulation,2013,30(1):329-332.
[12]刘芝怡,常睿.基于矩阵的不确定数据频繁项集快速挖掘算法[J].南京理工大学学报,2015,39(4):420-425.Liu Zhiyi,Chang Rui.Fast algorithm of frequent itemset mining based on matrix from uncertain data[J].Journal of Nanjing University of Science and Technology,2015,39(4):420-425.
[13]Prasad K S N,Ramakrishna S.Frequent pattern mining and current state of the art[J].International Journal of Computer Applications,2011,26(7):33-39.
[14]Jayanthi B,Duraiswamy K.A novel algorithm for cross level frequent pattern mining in multidatasets[J].International Journal of Computer Applications,2012,37(6):30-35.
[15]付冬梅,王志强.基于FP-tree和约束概念格的关联规则挖掘算法及应用研究[J].计算机应用研究,2014,31(4):1013-1019.
Fu Dongmei,Wang Zhiqiang.Mining algorithm of association rule based on FP-tree and constrained concept lattice and application research[J].Application Research of Computers,2014,31(4):1013-1019.
[16]Patro S N,Mishra S,Khuntia P,et al.Construction of FP tree using Huffman coding[J].International Journal of Computer Science Issues,2012,9(3):446-469.
[17]杨鹏坤,彭慧,周晓锋,等.改进的基于频繁模式树的最大频繁项集挖掘算法——FP-MFIA[J].计算机应用,2015,35(3):775-778.
Yang Pengkun,Peng Hui,Zhou Xiaofeng,et al.FP-MFIA:improved algorithm for mining maximum frequent itemsets based on frequent-pattern tree[J].Journal of Computer Applications,2015,35(3):775-778.
[18]Sharma Y,Tech M,SATI V M P,et al.Analysis and implementation of FP & Q-FP tree with minimum CPU utilization in association rule mining[J].International Journal of Computing,Communications and Networking,2012,1(1):39-44.
[19]阴爱英.基于线程并行计算的Apriori算法[J].西安科技大学学报,2014,34(1):71-74.
Yin Aiying.Apriori algorithm based on thread parallel computing[J].Journal of Xi’an University of Science and Technology,2014,34(1):71-74.
[20]林长方,吴扬扬,黄仲开,等.基于MapReduce的Apriori算法并行化[J].江南大学学报(自然科学版),2014,13(4):411-415.
Lin Changfang,Wu Yangyang,Huang Zhongkai,et al.Parallel research of Apriori algorithm based on MapReduce[J].Journal of Jiangnan University(Natural Science Edition),2014,13(4):411-415.

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Last Update: 2016-02-29