A Rough Set Approach to Incomplete Data

2015 ◽  
pp. 3-14 ◽  
Author(s):  
Jerzy W. Grzymala-Busse
Keyword(s):  
Rough Set ◽  
Incomplete Data

A Comparative Study Based on Rough Set and Classification Via Clustering Approaches to Handle Incomplete Data to Predict Learning Styles

2017 ◽  
Vol 9 (2) ◽  
pp. 1-20 ◽  
Author(s):  
Hemant Rana ◽  
Manohar Lal
Keyword(s):  
Learning Styles ◽  
Rough Set ◽  
Incomplete Data ◽  
Missing Values ◽  
Rough Set Theory ◽  
Decision Rules ◽  
Data Mining Tool ◽  
Knowledge Analysis ◽  
Clustering Approach ◽  
Mining Tool

Handling of missing attribute values are a big challenge for data analysis. For handling this type of problems, there are some well known approaches, including Rough Set Theory (RST) and classification via clustering. In the work reported here, RSES (Rough Set Exploration System) one of the tools based on RST approach, and WEKA (Waikato Environment for Knowledge Analysis), a data mining tool—based on classification via clustering—are used for predicting learning styles from given data, which possibly has missing values. The results of the experiments using the tools show that the problem of missing attribute values is better handled by RST approach as compared to the classification via clustering approach. Further, in respect of missing values, RSES yields better decision rules, if the missing values are simply ignored than the rules obtained by assigning some values in place of missing attribute values.

scholarly journals Factor reduction of quotation with rough set on incomplete data

2020 ◽  
Vol 48 ◽  
pp. 18-23 ◽  
Author(s):  
Qiunan Meng ◽  
Xun Xu ◽  
Luwei Yang
Keyword(s):  
Rough Set ◽  
Incomplete Data

scholarly journals A Novel Approach to Neutrosophic Soft Rough Set under Uncertainty

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 384 ◽  
Author(s):  
Ashraf Al-Quran ◽  
Nasruddin Hassan ◽  
Emad Marei
Keyword(s):  
Rough Set ◽  
Incomplete Data ◽  
Soft Set ◽  
Mathematical Tool ◽  
Soft Sets ◽  
Novel Approach ◽  
Proposed Model ◽  
Neutrosophic Logic ◽  
Soft Rough Set ◽  
Lower And Upper Approximations

To handle indeterminate and incomplete data, neutrosophic logic/set/probability were established. The neutrosophic truth, falsehood and indeterminacy components exhibit symmetry as the truth and the falsehood look the same and behave in a symmetrical way with respect to the indeterminacy component which serves as a line of the symmetry. Soft set is a generic mathematical tool for dealing with uncertainty. Rough set is a new mathematical tool for dealing with vague, imprecise, inconsistent and uncertain knowledge in information systems. This paper introduces a new rough set model based on neutrosophic soft set to exploit simultaneously the advantages of rough sets and neutrosophic soft sets in order to handle all types of uncertainty in data. The idea of neutrosophic right neighborhood is utilised to define the concepts of neutrosophic soft rough (NSR) lower and upper approximations. Properties of suggested approximations are proposed and subsequently proven. Some of the NSR set concepts such as NSR-definability, NSR-relations and NSR-membership functions are suggested and illustrated with examples. Further, we demonstrate the feasibility of the newly rough set model with decision making problems involving neutrosophic soft set. Finally, a discussion on the features and limitations of the proposed model is provided.

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.

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