Fast Distributed Mining Algorithm of Maximum Frequent Itemsets Based on Cloud Computing

被引:0
作者
He, Bo [1 ]
机构
[1] Chongqing Univ Technol, Sch Comp Sci & Engn, Chongqing 400054, Peoples R China
来源
INFORMATION COMPUTING AND APPLICATIONS, ICICA 2013, PT I | 2013年 / 391卷
关键词
Data Mining; Cloud Computing; Maximum Frequent Itemsets; LOCAL OUTLIERS;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The paper proposed a fast distributed mining algorithm of maximum frequent itemsets based on cloud computing, namely, FDMMFI algorithm. FDMMFI algorithm made nodes compute local maximum frequent itemsets by cloud computing, then the center node exchanged data with other nodes and combined, finally, global maximum frequent itemsets were gained by cloud computing. Theoretical analysis and experimental results suggest that under the same minimum support threshold, communication traffic and runtime of FDMMFI decreases while comparing with CD and FDM. The less the minimum support threshold, the better the three performance parameters of FDMMFI. FDMMFI algorithm is fast and effective.
引用
收藏
页码:407 / 416
页数:10
相关论文
共 25 条
[1]  
Aggarwal CC, 2001, SIGMOD RECORD, V30, P37
[2]   Parallel mining of association rules [J].
Agrawal, R ;
Shafer, JC .
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 1996, 8 (06) :962-969
[3]  
Agrawal R., 1994, P 20 INT C VERY LARG, VVolume 1215, P487
[4]  
[Anonymous], P INT C INF KNOWL MA
[5]  
Bayardo RJ, 2000, P ACM SIGMOD INT C M, P1
[6]  
Breunig MM, 1999, LECT NOTES ARTIF INT, V1704, P262
[7]   LOF: Identifying density-based local outliers [J].
Breunig, MM ;
Kriegel, HP ;
Ng, RT ;
Sander, J .
SIGMOD RECORD, 2000, 29 (02) :93-104
[8]  
Cheung DW, 1996, PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED INFORMATION SYSTEMS, P31, DOI 10.1109/PDIS.1996.568665
[9]  
Ester M., 1996, DENSITY BASED ALGORI, V96, P226
[10]  
Guha S., 1998, SIGMOD Record, V27, P73, DOI 10.1145/276305.276312