Rough and Fuzzy Set Based Classification Algorithm on Computer Practice Teaching Evaluation

2014 ◽  
Vol 678 ◽  
pp. 43-46
Author(s):  
Hong Xin Wan ◽  
Yun Peng
Keyword(s):  
Rough Set ◽  
Fuzzy Set ◽  
Evaluation System ◽  
Clustering Algorithm ◽  
Teaching Evaluation ◽  
Key Factors ◽  
Clustering Method ◽  
Practice Teaching ◽  
Evaluation Algorithm ◽  
Computer Teaching

Computer teaching should emphasize the engineering practicality, creativity, and pay more attention to the project and its application. It is important to evaluate the teaching effect. An evaluation system and corresponding algorithm are presented in this paper. There is a certain correlation between some evaluation factors, and the factors can be divided into key factors and secondary factors by rough set. The evaluation algorithm based on the key factors can reduce redundant factors and improve the efficiency. We designed a clustering algorithm based on fuzzy set on evaluation entities, which can reduce the dada size and improve the accuracy of the algorithm. Through the example analysis the algorithm of factors reduction based on rough set and clustering method based on fuzzy set are described in detail.

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.

A Fuzzy Set AHP-Based DFM Tool under Concurrent Engineering Environment

2007 ◽  
Vol 10-12 ◽  
pp. 145-149 ◽  
Author(s):  
H.G. Liu ◽  
Rong Mo ◽  
Qing Ming Fan ◽  
Zhi Yong Chang ◽  
Y. Zhao
Keyword(s):  
Fuzzy Set ◽  
Concurrent Engineering ◽  
Evaluation System ◽  
Smooth Transition ◽  
Analytic Hierarchy ◽  
Design And Manufacturing ◽  
Evaluation Algorithm ◽  
Engineering Environment ◽  
Engineering Effort ◽  
Manufacturability Evaluation

In the light of growing global competition, organizations around the world today are constantly under pressure to produce high-quality products at an economical price. The integration of design and manufacturing activities into one common engineering effort has been recognized as a key strategy for survival and growth. Design for manufacturability (DFM) is an approach to design that fosters the simultaneous involvement of product design and process design. The implementation of the DFM approach requires the collaboration of both the design and manufacturing functions within an organization. At present, For some reasons DFM approach is ineffectively including lack of interdisciplinary expertise of designers; inflexibility in organizational structure, which hinders interaction between design and manufacturing functions. Design for manufacture is the practice of designing products with manufacturing in mind. Early consideration of manufacturing issues can shorten product development cycle time, minimi overall development cost and ensure a smooth transition into production. In this paper, part manufacturability under Concurrent Engineering (CE) environment was analyzed in detail. An evaluation system of DFM was proposed according to CE ideas. A fuzzy set-based manufacturability evaluation algorithm is formulated to generate relative manufacturability indices to provide product designers with a better understanding of the relative ease or difficulty of machining the features in their designs. An analytic hierarchy process (AHP) method is introduced to assign weighting factors to features to reflect their functional importance. Results from the case studies show the method available and practicable.

Research on hesitant fuzzy clustering method based on fuzzy matroids

2021 ◽  
pp. 1-9
Author(s):  
Hui-Min Xiao ◽  
Mei-Qi Wang ◽  
Yan-Li Cao ◽  
Yu-Jie Guo
Keyword(s):  
Decision Making ◽  
Fuzzy Set ◽  
Clustering Algorithm ◽  
Decision Makers ◽  
Hesitant Fuzzy Set ◽  
Job Seekers ◽  
Clustering Method ◽  
Fuzzy Clustering Method ◽  
Cut Set ◽  
R Value

In this paper, to improve the situation of singleness of selecting results in hesitant fuzzy set decision-making and expand the range of choices for decision makers, we construct a hesitant fuzzy set clustering algorithm combined with fuzzy matroid operation. The algorithm synthesizes the r-cut set, fuzzy shrinking matroids in the fuzzy matroids and the operational properties of the fuzzy derived matroids, the r value also is used to connect the two types of fuzzy matroids to form a clustering algorithm. Finally, we apply the algorithm to the hesitant fuzzy set decision-making of job seekers choosing recruitment websites, each recruitment website as an optional scheme is divided into three categories of excellent to inferior schemes to provide job seekers with ideas and methods for favorably selecting recruitment websites.

scholarly journals (T, S)-Based Single-Valued Neutrosophic Number Equivalence Matrix and Clustering Method

Mathematics ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 36 ◽  
Author(s):  
Jiongmei Mo ◽  
Han-Liang Huang
Keyword(s):  
Fuzzy Clustering ◽  
Fuzzy Set ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Dual Pair ◽  
Neutrosophic Set ◽  
The Internet ◽  
Clustering Method ◽  
Comparison Experiment ◽  
Neutrosophic Number

Fuzzy clustering is widely used in business, biology, geography, coding for the internet and more. A single-valued neutrosophic set is a generalized fuzzy set, and its clustering algorithm has attracted more and more attention. An equivalence matrix is a common tool in clustering algorithms. At present, there exist no results constructing a single-valued neutrosophic number equivalence matrix using t-norm and t-conorm. First, the concept of a ( T , S ) -based composition matrix is defined in this paper, where ( T , S ) is a dual pair of triangular modules. Then, a ( T , S ) -based single-valued neutrosophic number equivalence matrix is given. A λ -cutting matrix of single-valued neutrosophic number matrix is also introduced. Moreover, their related properties are studied. Finally, an example and comparison experiment are given to illustrate the effectiveness and superiority of our proposed clustering algorithm.

Non Metric Model Based on Rough Set Representation

2013 ◽  
Vol 17 (4) ◽  
pp. 540-551 ◽  
Author(s):  
Yasunori Endo ◽  
◽  
Ayako Heki ◽  
Yukihiro Hamasuna ◽  
◽  
...  
Keyword(s):  
Rough Set ◽  
Fuzzy Set ◽  
Rough Sets ◽  
Clustering Algorithms ◽  
Clustering Method ◽  
Numerical Examples ◽  
Upper Approximation ◽  
Membership Grade ◽  
Fuzzy Degree ◽  
Metric Model

The non metricmodel is a kind of clustering method in which belongingness or the membership grade of each object in each cluster is calculated directly from dissimilarities between objects and in which cluster centers are not used. The clustering field has recently begun to focus on rough set representation instead of fuzzy set representation. Conventional clustering algorithms classify a set of objects into clusters with clear boundaries, that is, one object must belong to one cluster. Many objects in the real world, however, belong to more than one cluster because cluster boundaries overlap each other. Fuzzy set representation of clusters makes it possible for each object to belong to more than one cluster. The fuzzy degree of membership may, however, be too descriptive for interpreting clustering results. Rough set representation handles such cases. Clustering based on rough sets could provide a solution that is less restrictive than conventional clustering and more descriptive than fuzzy clustering. This paper covers two types of Rough-set-based Non Metric model (RNM). One algorithm is the Roughset-based Hard Non Metric model (RHNM) and the other is the Rough-set-based Fuzzy Non Metric model (RFNM). In both algorithms, clusters are represented by rough sets and each cluster consists of lower and upper approximation. The effectiveness of proposed algorithms is evaluated through numerical examples.

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