Abstract
Attribute reduction, being a complex problem in data mining, has attracted many researchers. The importance of this issue rises due to ever-growing data to be mined. Together with data growth, a need for speeding up computations increases. The contribution of this paper is twofold: (1) investigation of breadth search strategies for finding minimal reducts in order to emerge the most promising method for processing large data sets; (2) development and implementation of the first hardware approach to finding minimal reducts in order to speed up time-consuming computations. Experimental research showed that for software implementation blind breadth search strategy is in general faster than frequency-based breadth search strategy not only in finding all minimal reducts but also in finding one of them. An inverse situation was observed for hardware implementation. In the future work, the implemented tool is to be used as a fundamental module in a system to be built for processing large data sets.
Highlights
Feature selection, especially attribute reduction [26], seems to be a more essential data preprocessing task than ever before
The contribution of this paper is twofold: (1) investigation of breadth search strategies for finding minimal reducts in order to emerge the most promising method for processing large data sets; (2) development and implementation of the first hardware approach to finding minimal reducts in order to speed up time-consuming computations
The goal of this paper is to provide an efficient approach for finding minimal reducts
Summary
Especially attribute reduction [26], seems to be a more essential data preprocessing task than ever before. In the Big Data era, reducing a data set, even by one feature/attribute, may significantly influence the data size or/and time needed for processing it. In spite of the fact that reducing as many attributes as possible of a large data set may be expensive itself, benefits that come from further processing a much smaller data set can be incomparably greater. Attribute reduction has extensively been investigated from a theoretical and practical viewpoint In spite of the existence of a rich literature, attribute reduction is still a hot topic; a new area of its application and new methods of its improvement are constantly discovered In spite of the existence of a rich literature, attribute reduction is still a hot topic; a new area of its application and new methods of its improvement are constantly discovered (e.g. [3, 5, 14, 15, 22, 25, 39]).
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