A Hybrid Method for Fast Finding the Reduct with the Best Classification Accuracy

Abstract

Usually a dataset has a lot of reducts finding all of which is known to be an NP hard problem. On the other hand, different reducts of a dataset may provide different classification accuracies. Usually, for every dataset, there is only a reduct with the best classification accuracy to obtain this best one, firstly we obtain the group of attributes that are dominant for the given dataset by using the decision tree algorithm. Secondly we complete this group up to reducts by using discernibility function techniques. Finally, we select only one reduct with the best classification accuracy by using data mining classification algorithms. The experimental results for datasets indicate that the classification accuracy is improved by removing the irrelevant features and using the simplified attribute set which is derived from proposed method

Prediction of users webpage access behaviour using association rule mining

“…Association rules are employed today in many application areas, an area of specific interest being Web usage mining (Agrawal & Srikant 1994;Kriegel 2007). Association rules are usually required to satisfy a user-specified minimum support and a user-specified minimum confidence simultaneously (Kum et al 2005;Hacibeyoglu et al 2013;Ganapathy et al 2014;Zhou & Huang 2014). Association rule generation is usually split up into two separate steps: First, the minimum support being applied to find all frequent itemsets in a database.…”