Mining association rules on significant rare data using relative support

被引：83
|
作者：
Yun, HY
Ha, DS
Hwang, BY
Ryu, KH [1 ]
机构：
[1] Chungbuk Natl Univ, Sch Elect & Comp Engn, Cheongju 361763, South Korea
[2] Chonnam Natl Univ, Dept Comp Sci, Kwangju, South Korea
[3] LG Elect Inc, Seoul, South Korea
来源：
JOURNAL OF SYSTEMS AND SOFTWARE | 2003年 / 67卷 / 03期
关键词：
data mining; potential information; association rules; significant rare data;
D O I：
10.1016/S0164-1212(02)00128-0
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
Recently, data mining, a technique to analyze the stored data in large databases to discover potential information and knowledge, has been a popular topic in database research. In this paper, we study the techniques discovering the association rules which are one of these data mining techniques. And we propose a technique discovering the association rules for significant rare data that appear infrequently in the database but are highly associated with specific data. Furthermore, considering these significant rare data, we evaluate the performance of the proposed algorithm by comparing it with other existing algorithms for discovering the association rules. (C) 2002 Elsevier Inc. All rights reserved.
引用
收藏
页码：181 / 191
页数：11
相关论文
共 50 条
  • [1] Mining significant association rules from educational data using critical relative support approach
    Abdullah, Zailani
    Herawan, Tutut
    Ahmad, Noraziah
    Deris, Mustafa Mat
    [J]. WORLD CONFERENCE ON EDUCATIONAL TECHNOLOGY RESEARCHES-2011, 2011, 28
  • [2] Association rules on significant rare data using second support
    Mustafa, MD
    Nabila, NF
    Evans, DJ
    Saman, MY
    Mamat, A
    [J]. INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS, 2006, 83 (01) : 69 - 80
  • [3] Mining significant association rules from uncertain data
    Anshu Zhang
    Wenzhong Shi
    Geoffrey I. Webb
    [J]. Data Mining and Knowledge Discovery, 2016, 30 : 928 - 963
  • [4] Mining significant association rules from uncertain data
    Zhang, Anshu
    Shi, Wenzhong
    Webb, Geoffrey I.
    [J]. DATA MINING AND KNOWLEDGE DISCOVERY, 2016, 30 (04) : 928 - 963
  • [5] Mining association rules using relative confidence
    Do, TD
    Hui, CS
    Fong, ACM
    [J]. INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING IDEAL 2004, PROCEEDINGS, 2004, 3177 : 306 - 313
  • [6] Data mining based decision support system to support association rules
    University of Ljubljana, Faculty of Computer and Information Science, Tržaška 25, 1000 Ljubljana
    [J]. Elektroteh Vestn Electrotech Rev, 2007, 4 (195-200):
  • [7] Data Mining Based Decision Support System to Support Association Rules
    Rupnik, Rok
    Kukar, Matjaz
    [J]. ELEKTROTEHNISKI VESTNIK-ELECTROCHEMICAL REVIEW, 2007, 74 (04): : 195 - 200
  • [8] Mining fuzzy association rules using partial support
    Xu, LJ
    Xie, KL
    [J]. 2003 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-5, PROCEEDINGS, 2003, : 113 - 117
  • [9] Mining Weighted Rare Association Rules Using Sliding Window over Data Streams
    Ouyang, Weimin
    [J]. PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ELECTRONIC TECHNOLOGY, 2016, 48 : 116 - 119
  • [10] Mining significant association rules (short version)
    Li, JY
    Shen, H
    Pritchard, P
    [J]. INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED PROCESSING TECHNIQUES AND APPLICATIONS, VOLS I-V, PROCEEDINGS, 1999, : 1458 - 1461
12345