Prioritization of association rules in data mining: Multiple criteria decision approach

被引:30
|
作者
Choi, DH
Ahn, BS
Kim, SH
机构
[1] Korea Adv Inst Sci & Technol, Grad Sch Management, Seoul 130012, South Korea
[2] Hansung Univ, Dept Business Adm, Seoul 136792, South Korea
关键词
rule prioritization; rule conflict; association rule mining; ELECTRE;
D O I
10.1016/j.eswa.2005.06.006
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Data mining techniques, extracting patterns from large databases are the processes that focus on the automatic exploration and analysis of large quantities of raw data in order to discover meaningful patterns and rules. In the process of applying the methods, most of the managers who are engaging the business encounter a multitude of rules resulted from the data mining technique. In view of multi-faceted characteristics of such rules, in general, the rules are featured by multiple conflicting criteria that are directly related with the business values, such as, e.g. expected monetary value or incremental monetary value. In the paper, we present a method for rule prioritization, taking into account the business values which are comprised of objective metric or managers' subjective judgments. The proposed methodology is an attempt to make synergy with decision analysis techniques for solving problems in the domain of data mining. We believe that this approach would be particularly useful for the business managers who are suffering from rule quality or quantity problems, conflicts between extracted rules, and difficulties of building a consensus in case several managers are involved for the rule selection. (c) 2005 Elsevier Ltd. All rights reserved.
引用
收藏
页码:867 / 878
页数:12
相关论文
共 50 条
  • [31] Mining association rules approach with multiple minimum supports using maximum constraints
    Department of Information, Zhijiang College, Zhejiang University of Technology, Hangzhou 310024, China
    不详
    不详
    Jisuanji Gongcheng, 2006, 11 (103-105):
  • [32] Mining Fuzzy Multiple-Level Association Rules from Quantitative Data
    Tzung-Pei Hong
    Kuei-Ying Lin
    Been-Chian Chien
    Applied Intelligence, 2003, 18 : 79 - 90
  • [33] Mining fuzzy multiple-level association rules from quantitative data
    Hong, TP
    Lin, KY
    Chien, BC
    APPLIED INTELLIGENCE, 2003, 18 (01) : 79 - 90
  • [34] Mining Generalized Association Rules with Quantitative Data under Multiple Support Constraints
    Lee, Yeong-Chyi
    Hong, Tzung-Pei
    Chen, Chun-Hao
    COMPUTATIONAL COLLECTIVE INTELLIGENCE: TECHNOLOGIES AND APPLICATIONS, PT II, 2010, 6422 : 224 - 231
  • [35] APRIORI MULTIPLE ALGORITHM FOR MINING ASSOCIATION RULES
    Stanisic, Predrag
    Tomovic, Savo
    INFORMATION TECHNOLOGY AND CONTROL, 2008, 37 (04): : 311 - 320
  • [36] Data Mining Framework for Generating Sales Decision Making Information Using Association Rules
    Kabir, Md. Humayun
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2016, 7 (05) : 378 - 385
  • [37] Collaborative mining method of traffic accident data based on decision tree and association rules
    Liu F.H.
    Advances in Transportation Studies, 2023, 1 (Special Issue): : 73 - 86
  • [38] A majority rules approach to data mining
    Roiger, RJ
    Azarbod, C
    Sant, RR
    INTELLIGENT INFORMATION SYSTEMS, (IIS'97) PROCEEDINGS, 1997, : 100 - 107
  • [39] Mining diabetes database with decision trees and association rules
    Zorman, M
    Masuda, G
    Kokol, P
    Yamamoto, R
    Stiglic, B
    PROCEEDINGS OF THE 15TH IEEE SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS, 2002, : 134 - 139
  • [40] SWARM: An Approach for Mining Semantic Association Rules from Semantic Web Data
    Barati, Molood
    Bai, Quan
    Liu, Qing
    PRICAI 2016: TRENDS IN ARTIFICIAL INTELLIGENCE, 2016, 9810 : 30 - 43