Attribute Reduction for Concepts and Concept Drifting Detection in Heterogeneous Data

doi:10.3969/j.issn.0372-2112.2018.05.032

您当前的位置：

首页 >

文章列表页 >

Attribute Reduction for Concepts and Concept Drifting Detection in Heterogeneous Data

更新时间：2025-07-08

- Attribute Reduction for Concepts and Concept Drifting Detection in Heterogeneous Data
- Acta Electronica Sinica Vol. 46, Issue 5, Pages: 1234-1239(2018)
- 作者机构：
  
  1. 浙江师范大学数理与信息工程学院,浙江,金华,321004
  2. 北京交通大学计算机与信息技术学院,北京,100044
  3. 浙江师范大学行知学院,浙江,金华,321004
  4. 浙江师范大学数理与信息工程学院,浙江,金华,321004
  5. 北京交通大学计算机与信息技术学院,北京,100044
  6. 浙江师范大学行知学院,浙江,金华,321004
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2018.05.032
  CLC： TP18
- Published Online：25 May 2018，
  
  Published：2018
- 稿件说明：
移动端阅览
Attribute Reduction for Concepts and Concept Drifting Detection in Heterogeneous Data[J]. Acta Electronica Sinica, 2018, 46(5): 1234-1239.
DOI：

Attribute Reduction for Concepts and Concept Drifting Detection in Heterogeneous Data[J]. Acta Electronica Sinica, 2018, 46(5): 1234-1239. DOI： 10.3969/j.issn.0372-2112.2018.05.032.

摘要

粗糙集是粒计算的一种重要方法，数据异构性是大数据的一种特征.针对异构数据问题，探索了粗糙集属性约简的本质，提出了概念属性约简的定义，它兼容值约简、Pawlak约简和并行约简.探究了概念属性约简的性质，提出了异构数据的属性约简方法和概念漂移探测方法.理论分析和示例表明了这些方法的有效性.为粗糙集、粒计算融入大数据的时代潮流提供了一种新方法.

Abstract

Rough set theory is one of important methods of granular computing

and data heterogeneities are one of remarkable characteristics in big data.For data heterogeneities

we define attribute reduction for concepts after investigating intrinsic quality of attribute reducts

which can contain value reducts

Pawlak attribute reducts and parallel reducts.After investigating properties of concept-attribute-reduction

we present a new method to reduce redundant attributes and a new method to detect concept drift for heterogeneous concepts.Theoretical analysis and examples show that these methods are valid.This work provides a new type way for rough set theory and granular computing to integrate into big data.

关键词

Keywords

references

Views

155

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Multi-Level Description of Granules From an Outsider’s Perspective

Survey on Attribute Reduction Algorithm of Rough Set

Knowledge Acquisition from Decision Tables Containing Continuous-Valued Attributes

Efficient Rough Attribute Reduction Based on Quantum Frog-Leaping Co-Evolution

Related Author

ZHI Hui-lai

ZHANG Li

LI Jin-hai

ZHOU Tao

LU Hui-ling

HUO Bing-qiang

REN Hai-ling

FONT

Related Institution

Data Science Research Center, Kunming University of Science and Technology

School of Computer Science and Technology, Henan Polytechnic University

School of Computer Science and Engineering， North Minzu University

School of Science， Ningxia Medical University

Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission， North Minzu University

⁰