Abstract
The main objective of the Attribute Reduction problem in Rough Set Theory is to find and retain the set of attributes whose values vary most between objects in an Information System or Decision System. Besides, Mining Frequent Patterns aims finding items that the number of times they appear together in transactions exceeds a given threshold as much as possible. Therefore, the two problems have similarities. From that, an idea formed is to solve the problem of Attribute Reduction from the viewpoint and method of Mining Frequent Patterns. The main difficulty of the Attribute Reduction problem is the time consuming for execution, NP-hard. This article proposes two new algorithms for Attribute Reduction: one has linear complexity, and one has global optimum with concepts of Maximal Random Prior Set and Maximal Set.
Highlights
Attribute reduction has played an important role in rough set applied in many fields, such as data mining, pattern recognition, machine learning
Rule induction can be applied in rough set theory due to attribute reduction algorithms (Yao and Zhao 2008) (Ju et al 2011)
An attribute reduction algorithm based on genetic algorithm with improved selection operator and discernibility matrix was researched and introduced (Zhenjiang et al 2012)
Summary
Attribute reduction has played an important role in rough set applied in many fields, such as data mining, pattern recognition, machine learning. An attribute reduction algorithm based on genetic algorithm with improved selection operator and discernibility matrix was researched and introduced (Zhenjiang et al 2012). To deal with these systems, a paper proposed a new attribute reduction method based on information quantity This approach improved traditional tolerance relationship calculation methods using an extension of tolerance relationship in rough set theory (Xu et al 2012). An algorithm based on rough set and Wasp Swarm Optimization was introduced It utilizes mutual information based information entropy to find core attributes, and utilizes the significance of feature as probability information to search through the feature space for minimum attributes reduction result (Fan and Zhong 2012). This article introduces an algorithm based on bit-chains and maximal random prior set It finds out a reduction with linear time but the result is not global optimization. Another algorithm based on maximal set (a new development of maximal random prior set) and the algorithm for Accumulating Frequent Pattern (Nguyen TT and Nguyen PK 2013) to find a global optimal reduction is proposed
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