| Peer-Reviewed

Parameter Selection Strategy for Frequent Itemsets in Association Analysis

Received: 16 March 2020     Accepted: 8 April 2020     Published: 12 May 2020
Views:       Downloads:
Abstract

In data mining, association analysis mainly deals with different associations between things. Different degrees of correlation are usually treated differently in performance. In a production society, people are more interested in understanding the strong relationships between things, while ignoring weaker relationships, thereby making meaningful and valuable decisions. However, people must face several problems. For example, how to use parameters to define strong correlation; how to define meaningful parameters, this article uses experiments to explain the main factors affecting the parameters and how to select parameter values. Find the balance point where the application association produces economic value, then this balance point is a more meaningful parameter. The purpose of this article is to find the support and credibility based on association analysis through dichotomy, and compare the application analysis of the same metric value in different scenarios. Experimental results show that selecting the same parameter value in different scenarios' associated demand analysis (such as attribute association analysis) will not produce the same benefit. In the same scenario, the dichotomy method can make the parameter value close to a more meaningful value. Therefore, how to define the parameters of frequent itemsets to produce the maximum benefit is the significance of this article.

Published in American Journal of Mathematical and Computer Modelling (Volume 5, Issue 2)
DOI 10.11648/j.ajmcm.20200502.13
Page(s) 47-50
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

Frequent Itemsets, Support, Credibility, Parameter Settings

References
[1] Wang Shuang, Yang Guangming, Zhu Zhiliang. Frequent item query algorithm based on uncertain data [J]. Journal of Northeastern University (Natural Science). 2011 (03).
[2] Yan Yuejin, Li Zhoujun, Chen Huowang. Efficient mining of maximum frequent itemsets based on FP-Tree [J]. Journal of Software. No. 2, 2005.
[3] Feng Yucai, Feng Jianlin. Incremental Updating Algorithm for Association Rules [J]. Journal of Software. 1998 (04).
[4] Song Yuqing, Zhu Yuquan, Sun Zhihui, Chen Geng. Algorithm for Mining and Updating the Maximum Frequent Itemsets Based on FP-Tree [J]. Journal of Software. 2003 (09).
[5] Cui Haili, Yuan Zhaoshan. A mining algorithm to quickly find the maximum frequent itemsets [J]. Journal of Hefei University of Technology (Natural Science Edition). 2006 (11).
[6] Wang Jinmiao, Zhang Longbo, Yan Guanghui, Wang Fengying. A Method for Mining the Maximum Frequent Itemsets in Uncertain Data [J]. Journal of Shandong University of Technology (Natural Science Edition). 2013 (05).
[7] Ma Lisheng, Deng Huiwen, Qi Yi. Mining algorithm of maximum frequent itemsets based on FP-tree [J]. Computer Engineering and Design. 2008 (02).
[8] Liu Junqiang, Sun Xiaoying, Wang Xun, Pan Yunhe. A New Method for Mining Maximum Frequent Patterns [J]. Chinese Journal of Computers. 2004 (10).
[9] Li Xiaoqing. Analysis and mining of customer association risk based on big data [J]. The era of financial technology. 2020 (04).
[10] Sun Keke; Li Zhong; Li Haiyang; Li Ying; Wang Yuanyuan University Library Access Control Data and Results Association Analysis [J]. Computer Knowledge and Technology. 2020 (02).
[11] Xiang Jianfeng, Research on the Architecture of Network Security Situation Awareness Platform Based on Big Data [J]. Science and Technology Innovation. 2020 (02).
[12] Wang Xiangrui. Application research of association rules mining in data mining technology [J]. Coal Technology. 2011 (08).
[13] Shen Yi, Wang Shuwang. Research on the mining of quantitative association rules [J]. Computer and Information Technology. 2005 (05).
[14] Li Xucheng, Wang Baobao. An improvement of Apriori algorithm in mining association rules [J]. Computer Engineering. 2002 (07).
[15] Zheng Lin. A divide-and-conquer Apriori algorithm that directly generates frequent itemsets [J]. Computer Applications and Software. 2014 (04). Biography.
Cite This Article
  • APA Style

    Yuan Hai Yan. (2020). Parameter Selection Strategy for Frequent Itemsets in Association Analysis. American Journal of Mathematical and Computer Modelling, 5(2), 47-50. https://doi.org/10.11648/j.ajmcm.20200502.13

    Copy | Download

    ACS Style

    Yuan Hai Yan. Parameter Selection Strategy for Frequent Itemsets in Association Analysis. Am. J. Math. Comput. Model. 2020, 5(2), 47-50. doi: 10.11648/j.ajmcm.20200502.13

    Copy | Download

    AMA Style

    Yuan Hai Yan. Parameter Selection Strategy for Frequent Itemsets in Association Analysis. Am J Math Comput Model. 2020;5(2):47-50. doi: 10.11648/j.ajmcm.20200502.13

    Copy | Download

  • @article{10.11648/j.ajmcm.20200502.13,
      author = {Yuan Hai Yan},
      title = {Parameter Selection Strategy for Frequent Itemsets in Association Analysis},
      journal = {American Journal of Mathematical and Computer Modelling},
      volume = {5},
      number = {2},
      pages = {47-50},
      doi = {10.11648/j.ajmcm.20200502.13},
      url = {https://doi.org/10.11648/j.ajmcm.20200502.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmcm.20200502.13},
      abstract = {In data mining, association analysis mainly deals with different associations between things. Different degrees of correlation are usually treated differently in performance. In a production society, people are more interested in understanding the strong relationships between things, while ignoring weaker relationships, thereby making meaningful and valuable decisions. However, people must face several problems. For example, how to use parameters to define strong correlation; how to define meaningful parameters, this article uses experiments to explain the main factors affecting the parameters and how to select parameter values. Find the balance point where the application association produces economic value, then this balance point is a more meaningful parameter. The purpose of this article is to find the support and credibility based on association analysis through dichotomy, and compare the application analysis of the same metric value in different scenarios. Experimental results show that selecting the same parameter value in different scenarios' associated demand analysis (such as attribute association analysis) will not produce the same benefit. In the same scenario, the dichotomy method can make the parameter value close to a more meaningful value. Therefore, how to define the parameters of frequent itemsets to produce the maximum benefit is the significance of this article.},
     year = {2020}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Parameter Selection Strategy for Frequent Itemsets in Association Analysis
    AU  - Yuan Hai Yan
    Y1  - 2020/05/12
    PY  - 2020
    N1  - https://doi.org/10.11648/j.ajmcm.20200502.13
    DO  - 10.11648/j.ajmcm.20200502.13
    T2  - American Journal of Mathematical and Computer Modelling
    JF  - American Journal of Mathematical and Computer Modelling
    JO  - American Journal of Mathematical and Computer Modelling
    SP  - 47
    EP  - 50
    PB  - Science Publishing Group
    SN  - 2578-8280
    UR  - https://doi.org/10.11648/j.ajmcm.20200502.13
    AB  - In data mining, association analysis mainly deals with different associations between things. Different degrees of correlation are usually treated differently in performance. In a production society, people are more interested in understanding the strong relationships between things, while ignoring weaker relationships, thereby making meaningful and valuable decisions. However, people must face several problems. For example, how to use parameters to define strong correlation; how to define meaningful parameters, this article uses experiments to explain the main factors affecting the parameters and how to select parameter values. Find the balance point where the application association produces economic value, then this balance point is a more meaningful parameter. The purpose of this article is to find the support and credibility based on association analysis through dichotomy, and compare the application analysis of the same metric value in different scenarios. Experimental results show that selecting the same parameter value in different scenarios' associated demand analysis (such as attribute association analysis) will not produce the same benefit. In the same scenario, the dichotomy method can make the parameter value close to a more meaningful value. Therefore, how to define the parameters of frequent itemsets to produce the maximum benefit is the significance of this article.
    VL  - 5
    IS  - 2
    ER  - 

    Copy | Download

Author Information
  • Huashang College, Guangdong University of Finance & Economics, Guangzhou, China

  • Sections