Computer Engineering and Applications ›› 2017, Vol. 53 ›› Issue (21): 68-71.DOI: 10.3778/j.issn.1002-8331.1605-0246

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Matrix-based incremental reduction approach with attribute values refining

LI Dan   

  1. Department of Computer Science and Technology , Chengdu Neusoft University, Qingchengshan, Sichuan 611844, China
  • Online:2017-11-01 Published:2017-11-15


李  丹   

  1. 成都东软学院 计算机科学与技术系,四川 青城山 611844

Abstract: In practices, many real data in databases may vary dynamically. One has to run a knowledge acquisition method repeatedly in order to acquire new knowledge. This is very time-consuming. To overcome this deficiency, incremental approaches have been presented to deal with dynamic data set. This paper proposes a matrix-based incremental reduction approach with attribute values refining. When a part of data in a given data set is replaced by some new data, compared with the non-incremental reduction approach, the developed incremental reduction approach can find a new reduct in a much shorter time. Finally, experiments on two data sets downloaded from UCI show that the developed algorithm is effective.

Key words: attribute values refining, incremental learning, attribute reduction, rough set, knowledge granularity

摘要: 现实生活中许多数据库都是动态变化的,为了获取新的知识,传统的方法需要重复计算,耗时巨大。为了克服这个缺陷,有效处理动态数据,许多学者提出了增量学习方法。针对决策表属性值动态变化,提出了基于属性值细化的矩阵增量约简算法,当一部分属性值被细化时,同非增量约简方法相比,增量方法能快速找到新的约简,最后通过UCI数据进行性能测试,实验仿真结果表明所提增量约简算法是有效的。

关键词: 属性值细化, 增量学习, 属性约简, 粗糙集, 知识粒度