The Further Development of Weka Base on Positive and Negative Association Rules
Yanguang Shen, Jie Liu, Jing Shen · 2010
This paper introduced the features, functions, and mining process of the open-source data mining platform of Weka. In order to overcome the weakness of the aspects of association rules in Weka system research, we used positive and negative association rules algorithm to embed into the Weka platform, and expanded the association rules algorithm under the open-source environment for the further development. We contrasted and analyzed the embedded algorithm with the original algorithm of association rules, taking full advantage of the functions of class and visualization in the open-source platform of Weka. This algorithm was made improvements in both extracting the explicit rules and fully mining the implicit rules. We carried out the experiment of public intelligence and information systems to obtain better results of association rules, and verified the good adaptability and scalability of the data mining platform of Weka based on the positive and negative association rules.