From Visual Analytics and Applications
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| − | <b>VRshiny</b>: An Application for better business decision making - <b>V</b>isualizing Association <b>R</b>ules with Network Diagram in <b>Shiny</b> | + | <b>VRshiny</b>: |
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| − | <br>Association rule mining is a rule-based machine learning method which is meant to detect frequent patterns, correlations, associations, or causal structures from data sets found in various kinds of databases such as relational databases, transactional databases, and other forms of data repositories. arules is a robust association rule mining package of R. The richness of its functions is comparable to, if not more superior than the expensive commercial-of-the-shelves analytical toolkit such as SAS Enterprise Miner and IBM-SPSS Modeler. However, the usage of arules package tends to be confined within academic research. This is because the effective used of arules package required intermediate R programming skill which is not commonly available in the business analyst community. | + | <br> |
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| − | <br>In view of this limitation, our project seeks to provide an user-interface to arules package by using R Shiny framework. The user-friendly interface design allows casual users to manage, explore, calibrate and visualise complex items mining and association rules mining models without having to type a single line of code. Besides providing user-friendly interface, our application also incorporates an interactive graph visualisation method to enhance the interpret-ability of the outputs of frequent itemsets mining and association rules mining algorithms. | + | <br> |
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| − | <br>This presentation consists of five main sections. Firstly, the motivation and objectives of the project will be discussed. This is followed by a detailed discussion on the principles and concepts of association rule mining and the R packages used to perform association rules mining, the arules family of packages. Thirdly, the application and visualization design with respect to the improvements made to the arules visualization packages will be discussed. Following which, we will demonstrate the flexible use of our application with two different use cases. The presentation will conclude with a sharing of valuable insights gained through working on the project and potential application areas of our application.
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| − | * Bo Cao | + | * Huilin Yan |
| − | * Yuhui Zhou | + | * Xuelin Hou |
| − | * Yifei Guan | + | * Yanli Zhang |
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Revision as of 15:58, 23 October 2018
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VRshiny:
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- Huilin Yan
- Xuelin Hou
- Yanli Zhang
