Association rule mining method based on weighted frequent pattern tree in mobile computing environment

Jiang-hui Cai; Xu-jun Zhao; Ya-lin Xun

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Association rule mining method based on weighted frequent pattern tree in mobile computing environment

机译：移动计算环境中基于加权频繁模式树的关联规则挖掘方法

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摘要

With the development of mobile communication technology and computer technology, further requirements are put forward to provide decision support for the individual or customers on the move. Association rule mining is a way to solve this problem. Firstly, the association rule mining model in mobile computing is presented and then the concepts of weighted items and weighted association rules are proposed based on association rule mining in mobile computing environment. On this basis, weighted frequent pattern tree is given to mine weighted frequent itemsets in the mobile environment, at the same time a weighted frequent pattern tree construction algorithm and association rule extraction method are given, which provides a variety of auxiliary decision support services for mobile users.

机译：随着移动通信技术和计算机技术的发展，提出了进一步的要求以为移动中的个人或客户提供决策支持。关联规则挖掘是解决此问题的一种方法。首先提出了移动计算中的关联规则挖掘模型，然后基于移动计算环境中的关联规则挖掘，提出了加权项和加权关联规则的概念。在此基础上，将加权频繁模式树赋给移动环境中的加权频繁项集，同时给出了加权频繁模式树的构造算法和关联规则提取方法，为移动设备提供了多种辅助决策支持服务。用户。

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来源
《International journal of wireless and mobile computing》 |2013年第2期|193-199|共7页
作者
Jiang-hui Cai; Xu-jun Zhao; Ya-lin Xun;
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作者单位

Computer Science and Technology School, Taiyuan University of Science and Technology, Taiyuan, Shanxi, China;

Computer Science and Technology School, Taiyuan University of Science and Technology, Taiyuan, Shanxi, China;

Computer Science and Technology School, Taiyuan University of Science and Technology, Taiyuan, Shanxi, China;

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原文格式 PDF
正文语种 eng
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关键词
mobile computing; association rules; weights; frequent pattern;

机译：移动计算;关联规则;重量;频繁模式;

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1. An Improvised Frequent Pattern Tree Based Association Rule Mining Technique with Mining Frequent Item Sets Algorithm and a Modified Header Table [J] . Vandit Agarwal, Mandhani Kushal, Preetham Kumar International Journal of Data Mining & Knowledge Management Process . 2015,第2期

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2. Mining Weighted Frequent Patterns Based on an Improved Weighted FP-tree [J] . Yan Wang, rnHaiyan Xue Journal of information and computational science . 2010,第2期

机译：基于改进的加权FP树挖掘加权频繁模式
3. A Secure Association Rules Mining Scheme Based on Frequent-Pattern Tree [J] . Chunhua SU, Kouichi SAKURAI 電子情報通信学会技術研究報告 . 2008,第162期

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4. A Frequent Pattern Mining Algorithm in Cloud Computing Environments using Association Rules Mining Algorithm [C] . Avinash Sharma, N. K. Tiwari International Conference on Recent Trends in Information Processing, Computing, Electrical and Electronics . 2017

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5. Design recovery and data mining: A methodology that identifies data-cohesive subsystems based on mining association rules. [D] . Montes de Oca, Carlos. 1999

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6. TSARM-UDP: An Efficient Time Series Association Rules Mining Algorithm Based on Up-to-Date Patterns [O] . Qiang Zhao, Qing Li, Deshui Yu, 2021

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7. An Improvised Frequent Pattern Tree Based Association Rule Mining Technique with Mining Frequent Item Sets Algorithm and a Modified Header Table [O] . Agarwal, Vandit, Kushal, Mandhani, Kumar, Dr. Preetham 2015

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Association rule mining method based on weighted frequent pattern tree in mobile computing environment

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