Improved data mining algorithms for frequent patterns with composite items

被引：0
|
作者：
Wang, Ke [1 ]
Liu, James N. K. [2 ]
Ma, Wei-Min [3 ]
机构：
[1] Beihang Univ, Sch Econ & Management, Beijing, Peoples R China
[2] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China
[3] Tongji Univ, Sch Econ & Management, Shanghai, Peoples R China
来源：
CHALLENGES IN INFORMATION TECHNOLOGY MANAGEMENT | 2008年
基金：
中国国家自然科学基金;
关键词：
D O I：
10.1142/9789812819079_0002
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
Mining association rules arc used to analyze the data in a database to discover interesting rules. The algorithms for mining association rules with composite items have the potential to discover rules which cannot be found out by algorithms without composite items. Algorithms for finding large composite items should scan the database for every candidate composite item to determine whether it is large. In this paper, we design some improved algorithms for finding large composite items which only need to scan the database one time to find all the large composite items. This algorithm also allows the reduction of many more redundant candidate composite items.
引用
收藏
页码：10 / +
页数：2
相关论文
共 50 条
  • [1] Frequent Pattern Mining Algorithms for Finding Associated Frequent Patterns for Data Streams: A Survey
    Nasreen, Shamila
    Azam, Muhammad Awais
    Shehzad, Khurram
    Naeem, Usman
    Ghazanfar, Mustansar Ali
    [J]. 5TH INTERNATIONAL CONFERENCE ON EMERGING UBIQUITOUS SYSTEMS AND PERVASIVE NETWORKS / THE 4TH INTERNATIONAL CONFERENCE ON CURRENT AND FUTURE TRENDS OF INFORMATION AND COMMUNICATION TECHNOLOGIES IN HEALTHCARE / AFFILIATED WORKSHOPS, 2014, 37 : 109 - +
  • [2] Efficient Algorithms with Time Fading Model for Mining Frequent Items over Data Stream
    Tu, Li
    Chen, Ling
    Zhang, Shan
    [J]. 2009 INTERNATIONAL CONFERENCE ON INDUSTRIAL AND INFORMATION SYSTEMS, PROCEEDINGS, 2009, : 403 - +
  • [3] Mining Robust Frequent Items in Data Streams
    Xia, Rui
    Dai, Haipeng
    Du, Zhanchao
    Li, Meng
    Liu, Alex X.
    Chen, Guihai
    [J]. 2020 IEEE INTERNATIONAL CONFERENCE ON JOINT CLOUD COMPUTING (JCC 2020), 2020, : 110 - 117
  • [4] Mining Frequent Composite Service Patterns
    Meng, Hui
    Wu, Lifa
    Zhang, Tianlei
    Chen, Guisheng
    Li, Deyi
    [J]. GCC 2008: SEVENTH INTERNATIONAL CONFERENCE ON GRID AND COOPERATIVE COMPUTING, PROCEEDINGS, 2008, : 713 - +
  • [5] A New Approach for Mining Frequent Items in Data Stream
    Tu, Li
    Chen, Ling
    [J]. 2010 INTERNATIONAL COLLOQUIUM ON COMPUTING, COMMUNICATION, CONTROL, AND MANAGEMENT (CCCM2010), VOL II, 2010, : 225 - 228
  • [6] Methods for mining frequent items in data streams: an overview
    Hongyan Liu
    Yuan Lin
    Jiawei Han
    [J]. Knowledge and Information Systems, 2011, 26 : 1 - 30
  • [7] Methods for mining frequent items in data streams: an overview
    Liu, Hongyan
    Lin, Yuan
    Han, Jiawei
    [J]. KNOWLEDGE AND INFORMATION SYSTEMS, 2011, 26 (01) : 1 - 30
  • [8] Frequent Items Mining Based on Weight in Data Stream
    Liang, Ran
    Sun, Jianling
    [J]. TENCON 2009 - 2009 IEEE REGION 10 CONFERENCE, VOLS 1-4, 2009, : 1808 - 1810
  • [9] Mining Longest Frequent Patterns with Low-Support Items
    Huang, Qinhua
    Ouyang, Weimin
    [J]. INTELLIGENT COMPUTING METHODOLOGIES, 2014, 8589 : 561 - 570
  • [10] AN IMPROVED ANT COLONY ALGORITHM FOR EFFECTIVE MINING OF FREQUENT ITEMS
    Sundaramoorthy, Suriya
    Shantharajah, S. P.
    [J]. JOURNAL OF WEB ENGINEERING, 2014, 13 (3-4): : 263 - 276
12345