scholarly journals Incremental Attribute Reduction Based on Knowledge Granularity under Incomplete Data

2021 ◽  
Vol 2025 (1) ◽  
pp. 012042
Author(s):  
Qiangqiang Zhong ◽  
Lei Wang ◽  
Wen Yang ◽  
Chao Liu
Keyword(s):  
Incomplete Data ◽  
Attribute Reduction ◽  
Knowledge Granularity

Construction and Application of Hierarchical Knowledge Granularity

2010 ◽  
Vol 143-144 ◽  
pp. 717-721
Author(s):  
Chun Feng Liu ◽  
Li Feng
Keyword(s):  
Neural Network ◽  
Cluster Analysis ◽  
Computational Complexity ◽  
Granular Computing ◽  
Attribute Reduction ◽  
Optimization Theory ◽  
Hierarchy Analysis ◽  
Speed Up ◽  
Construction Algorithms ◽  
Knowledge Granularity

As one aspect of granular computing, hierarchical knowledge granularity can speed up solution, and reduce computational complexity. This paper describes the structure and hierarchy analysis of granularity simply, details the current methods of construction algorithms in granular computing, and emphasizes the performance comparisons of various construction algorithms, and finally reviews the applications of knowledge granularity in rule extraction, attribute reduction, cluster analysis, optimization theory, neural network and fuzzy control and so on.

A Fuzzy Clustering Algorithm of Web Customers Based on Attributes Reduction

2014 ◽  
Vol 989-994 ◽  
pp. 1775-1778
Author(s):  
Hong Xin Wan ◽  
Yun Peng
Keyword(s):  
Rough Set ◽  
Fuzzy Set ◽  
Incomplete Data ◽  
Traditional Method ◽  
Clustering Algorithm ◽  
Uncertain Data ◽  
Attribute Reduction ◽  
Fuzzy Clustering Algorithm ◽  
Evaluation Algorithm ◽  
Data Objects

The evaluation algorithm is based on the attributes of data objects. There is a certain correlation between attributes, and attributes are divided into key attributes and secondary attributes. This paper proposes an algorithm of attribute reduction based on rough set and the clustering algorithm based on fuzzy set. The algorithm of attributes reduction based on rough set is described in detail first. There are a lot of uncertain data of customer clustering, so traditional method of classification to the incomplete data will be very complex. Clustering algorithm based on fuzzy set can improve the reliability and accuracy of web customers.

An efficient accelerator for attribute reduction from incomplete data in rough set framework

2011 ◽  
Vol 44 (8) ◽  
pp. 1658-1670 ◽  
Author(s):  
Yuhua Qian ◽  
Jiye Liang ◽  
Witold Pedrycz ◽  
Chuangyin Dang
Keyword(s):  
Rough Set ◽  
Incomplete Data ◽  
Attribute Reduction

An incremental attribute reduction approach based on knowledge granularity with a multi-granulation view

2017 ◽  
Vol 411 ◽  
pp. 23-38 ◽  
Author(s):  
Yunge Jing ◽  
Tianrui Li ◽  
Hamido Fujita ◽  
Zeng Yu ◽  
Bin Wang
Keyword(s):  
Attribute Reduction ◽  
Knowledge Granularity ◽  
Reduction Approach

scholarly journals Heuristic Attribute Reduction Based on Neighborhood Knowledge Granularity

2021 ◽  
Vol 2025 (1) ◽  
pp. 012043
Author(s):  
Wen Yang ◽  
Lei Wang ◽  
Chao Liu ◽  
Qiangqiang Zhong
Keyword(s):  
Attribute Reduction ◽  
Knowledge Granularity
Sign in / Sign up

Export Citation Format

Share Document