Volume 5, Issue 2, June 2020, Page: 47-50
Parameter Selection Strategy for Frequent Itemsets in Association Analysis
Yuan Hai Yan, Huashang College, Guangdong University of Finance & Economics, Guangzhou, China
Received: Mar. 16, 2020;       Accepted: Apr. 8, 2020;       Published: May 12, 2020
DOI: 10.11648/j.ajmcm.20200502.13      View  300      Downloads  87
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.
Keywords
Frequent Itemsets, Support, Credibility, Parameter Settings
To cite this article
Yuan Hai Yan, Parameter Selection Strategy for Frequent Itemsets in Association Analysis, American Journal of Mathematical and Computer Modelling. Vol. 5, No. 2, 2020, pp. 47-50. doi: 10.11648/j.ajmcm.20200502.13
Copyright
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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