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.

中文翻译：

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寻找最小化削减的广度搜索策略：硬件实现

摘要

属性约简是数据挖掘中的一个复杂问题，吸引了许多研究人员。由于要挖掘的数据不断增长，因此此问题的重要性日益提高。随着数据的增长，对加速计算的需求也在增加。本文的贡献有两个方面：（1）研究广度搜索策略以找到最小化的约简，从而形成处理大数据集的最有希望的方法；（2）开发和实现第一个硬件方法以找到最小的缩减量，以加快耗时的计算。实验研究表明，对于软件实现而言，盲广度搜索策略通常比基于频率的广度搜索策略更快，这不仅在于找到所有最小化还原，而且还在于找到其中一种。对于硬件实现，观察到相反的情况。在将来的工作中，已实现的工具将用作要处理大型数据集的系统中的基本模块。