Soft Maximal Association Rule for Web User Mining

被引：0
|
作者：
Yanto, Iwan Tri Riyadi [1 ]
Rahman, Arif [1 ]
Saaadi, Youes [2 ]
机构：
[1] Ahmad Dahlan Univ, Dept Informat Syst, Yogyakarta, Indonesia
[2] Univ Malaya, Dept Informat Syst, Kuala Lumpur, Malaysia
来源：
PROCEEDINGS OF 2016 2ND INTERNATIONAL CONFERENCE ON SCIENCE IN INFORMATION TECHNOLOGY (ICSITECH) - INFORMATION SCIENCE FOR GREEN SOCIETY AND ENVIRONMENT | 2016年
关键词：
web user transaction; association rule; soft maximal association rule; soft set; WORLD-WIDE-WEB; ROUGH SETS; MODEL;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Association rule mining of the web user transaction is one of important techniques for extracting information from web data, including its content, link, and user information using data mining tool. This technique finds a pattern and causal relation between items on given databases. The Maximal Association Rule is a data mining tools to determine the association rule the rough set theory based. Accordingly, the rough set can be defined in a form of soft set. This paper presents an implementation of Soft Maximal Association Rule which is the soft set theory based for web mining. The experiment shows that the computation of the proposed technique outperforms comparing to the baseline technique.
引用
收藏
页码：339 / 343
页数：5
相关论文
共 50 条
  • [1] Rough association rule mining in text documents for acquiring web user information needs
    Li, Yuefeng
    Zhong, Ning
    [J]. 2006 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE, (WI 2006 MAIN CONFERENCE PROCEEDINGS), 2006, : 226 - +
  • [2] Soft set based association rule mining
    Feng, Feng
    Cho, Junghoo
    Pedrycz, Witold
    Fujita, Hamido
    Herawan, Tutut
    [J]. KNOWLEDGE-BASED SYSTEMS, 2016, 111 : 268 - 282
  • [3] Web usage association rule mining system
    Dimitrijević, Maja
    Bošnjak, Zita
    [J]. Interdisciplinary Journal of Information, Knowledge, and Management, 2011, 6 : 137 - 150
  • [4] Soft Set Approach for Maximal Association Rules Mining
    Herawan, Tutut
    Yanto, Iwan Tri Riyadi
    Deris, Mustafa Mat
    [J]. DATABASE THEORY AND APPLICATION, 2009, 64 : 163 - 170
  • [5] Web Usage Mining using Fuzzy Association Rule
    Abhirami, K.
    [J]. FIRST INTERNATIONAL CONFERENCE ON EMERGING TRENDS IN ENGINEERING, TECHNOLOGY AND SCIENCE - ICETETS 2016, 2016,
  • [6] Bees Swarm Optimization for Web Association Rule Mining
    Djenouri, Y.
    Drias, H.
    Habbas, Z.
    Mosteghanemi, H.
    [J]. 2012 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY WORKSHOPS (WI-IAT WORKSHOPS 2012), VOL 3, 2012, : 142 - 146
  • [7] Multirelation Association Rule Mining on Datasets of the Web of Data
    de Oliveira, Felipe Alves
    Costa, Raquel Lopes
    Goldschmidt, Ronaldo R.
    Cavalcanti, Maria Claudia
    [J]. PROCEEDINGS OF THE XV BRAZILIAN SYMPOSIUM ON INFORMATION SYSTEMS, SBSI 2019: Complexity on Modern Information Systems, 2019,
  • [8] Integrating Web content clustering into Web log association rule mining
    Guo, J
    Keselj, V
    Gao, Q
    [J]. ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 2005, 3501 : 182 - 193
  • [9] An Association Rule Mining Approach for Libraries to Analyse User Interest
    Krishnamurthy, V.
    Balasubramani, R.
    [J]. 2014 INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING APPLICATIONS (ICICA 2014), 2014, : 122 - 125
  • [10] Visual analysis of user-driven association rule mining
    Chen, Wei
    Xie, Cong
    Shang, Pingping
    Peng, Qunsheng
    [J]. JOURNAL OF VISUAL LANGUAGES AND COMPUTING, 2017, 42 : 76 - 85
12345