Breadth search strategies for finding minimal reducts: towards hardware implementation

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.