Mining Indirect Positive and Negative Association Rules

被引：0
|
作者：
Ramasubbareddy, B. [1 ]
Govardhan, A. [2 ]
Ramamohanreddy, A. [3 ]
机构：
[1] Jyothishmathi Inst Technol & Sci, Karimnagar, India
[2] JNTUH Coll Engn, Karimnagar, India
[3] SV Univ, SVU Coll Engn, Tirupati, Andhra Pradesh, India
来源：
ADVANCES IN COMPUTING AND COMMUNICATIONS, PT I | 2011年 / 190卷
关键词：
Data mining; positive and negative association rules; indirect association;
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Indirect association is a new kind of infrequent pattern, which provides a new way for interpreting the value of infrequent patterns and can effectively reduce the number of uninteresting infrequent patterns. The concept of indirect association is to "indirectly" connect two rarely co-occurred items via a frequent itemset called mediator, and if appropriately utilized it can help to identify real interesting "infrequent itempairs" from databases. Indirect association rule is said to be positive (Negative) if mediator set contains presence (presence or absence) of items. Existing indirect association mining methods mine positive mediator sets. To the best of our knowledge, no research work has been conducted on mining indirect negative associations. In this paper, we propose an approach for mining indirect negative associations. The proposed method can discover all positive and negative indirect association between itemsets.
引用
收藏
页码：581 / +
页数：3
相关论文
共 50 条
  • [31] An incremental updating technique for mining indirect association rules
    Zheng, Cheng
    [J]. PROCEEDINGS OF 2008 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2008, : 217 - 221
  • [32] Negative and Positive Association Rules Mining from Text Using Frequent and Infrequent Itemsets
    Mahmood, Sajid
    Shahbaz, Muhammad
    Guergachi, Aziz
    [J]. SCIENTIFIC WORLD JOURNAL, 2014,
  • [33] Research on Association Rules Mining Base on Positive and Negative Items of FP-tree
    Chen, Chunwei
    Wang, Dandong
    [J]. PROCEEDINGS OF THE 2016 6TH INTERNATIONAL CONFERENCE ON MACHINERY, MATERIALS, ENVIRONMENT, BIOTECHNOLOGY AND COMPUTER (MMEBC), 2016, 88 : 1395 - 1399
  • [34] Mining a complete set of both positive and negative association rules from large databases
    Wang, Hao
    Zhang, Xing
    Chen, Guoqing
    [J]. ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS, 2008, 5012 : 777 - 784
  • [35] An efficient genetic algorithm for automated mining of both positive and negative quantitative association rules
    Bilal Alataş
    Erhan Akin
    [J]. Soft Computing, 2006, 10 : 230 - 237
  • [36] Mining Positive and Negative Association Rules in Multi-database Based on Minimum Interestingness
    Shang, Shi-ju
    Dong, Xiang-jun
    Li, Jie
    Zhao, Yuan-yuan
    [J]. INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION TECHNOLOGY AND AUTOMATION, VOL 1, PROCEEDINGS, 2008, : 791 - +
  • [37] Mining Positive and Negative Weighted Association Rules from Frequent Itemsets Based on Interest
    Jiang, He
    Zhao, Yuanyuan
    Dong, Xiangjun
    [J]. PROCEEDINGS OF THE 2008 INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN, VOL 2, 2008, : 242 - 245
  • [38] An Efficient Approach for Mining Positive and Negative Association Rules from Large Transactional Databases
    Kishor, Peddi
    Porika, Sammulal
    [J]. 2016 INTERNATIONAL CONFERENCE ON INVENTIVE COMPUTATION TECHNOLOGIES (ICICT), VOL 1, 2016, : 85 - 89
  • [39] An efficient genetic algorithm for automated mining of both positive and negative quantitative association rules
    Alatas, B
    Akin, E
    [J]. SOFT COMPUTING, 2006, 10 (03) : 230 - 237
  • [40] Mining Negative Association Rules in Distributed Environment
    Sahu, Aneesh K.
    Kumar, Raghvendra
    Rahim, Naazish
    [J]. 2015 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND COMMUNICATION NETWORKS (CICN), 2015, : 934 - 937
12345