New Similarity Measures between Vague Sets and Performance Analysis

2013 ◽  
Vol 811 ◽  
pp. 547-551 ◽  
Author(s):  
Hong Xu Wang ◽  
Hai Feng Wang ◽  
Kun Zhang ◽  
Hui Wang
Keyword(s):  
Data Mining ◽  
Performance Analysis ◽  
Similarity Measure ◽  
Similarity Measures ◽  
Practical Case ◽  
Vague Sets ◽  
Diagnosis Method ◽  
And Performance ◽  
Definition Of ◽  
New Formula

In order to amend the defects of existing similarity measure formula between vague sets, a new definition of similarity measure between vague sets is proposed and a new formula with higher resolution and highlighted uncertainty is presented on the basis of data mining vague value method. A general fault diagnosis method of Vague sets (GFDMVS) is proposed. The same practical case is studied with three methods and the results demonstrate the validity and reasonability of the method proposed in this paper.

scholarly journals A Novel Cosine Similarity Like Data Clustering Method for Effective Data Classification in Data Mining

2020 ◽  
Vol 9 (8) ◽  
pp. 340-346
Keyword(s):  
Data Mining ◽  
Similarity Measure ◽  
Categorical Data ◽  
Data Clustering ◽  
Similarity Measures ◽  
Numerical Data ◽  
Data Classification ◽  
Fundamental Goal ◽  
Learning Technique ◽  
Categorical Data Clustering

In data mining ample techniques use distance based measures for data clustering. Improving clustering performance is the fundamental goal in cluster domain related tasks. Many techniques are available for clustering numerical data as well as categorical data. Clustering is an unsupervised learning technique and objects are grouped or clustered based on similarity among the objects. A new cluster similarity finding measure, which is cosine like cluster similarity measure (CLCSM), is proposed in this paper. The proposed cluster similarity measure is used for data classification. Extensive experiments are conducted by taking UCI machine learning datasets. The experimental results have shown that the proposed cosinelike cluster similarity measure is superior to many of the existing cluster similarity measures for data classification.

Data mining artificial intelligence technology for college english test framework and performance analysis system

2020 ◽  
pp. 1-11
Author(s):  
Lin Shen
Keyword(s):  
Data Mining ◽  
Performance Analysis ◽  
Evaluation Model ◽  
Diagnostic Evaluation ◽  
Test Paper ◽  
Test Question ◽  
College English ◽  
Question Recommendation ◽  
And Performance ◽  
Analysis System

This article first studies and designs the college English test framework and performance analysis system. The author analyzes a large number of data collected by the system in three dimensions: using data mining title association models, using machine learning to merge college English score prediction models, and finally diagnosing on the basis of the sexual evaluation model, the author designed and implemented a test paper algorithm based on the association rules of the question type, and carried out relevant verification from the three aspects of test paper time, test question recommendation and improvement according to scores. Finally, according to the needs analysis, the author uses the diagnostic evaluation model and related test paper algorithm to design and implement the diagnostic evaluation model, which is added to the college English diagnostic practice system. It can be obtained through comparative experiments that the paper-based algorithm based on the diagnostic evaluation model proposed in this paper can effectively give better practice guidance and test question recommendation to the learner’s learning status and knowledge point problem obstacles, and can effectively improve learning. The achievements of the authors have broad application prospects and research value.

Vague Pattern Recognition Algorithm and its Irrigation System

2012 ◽  
Vol 433-440 ◽  
pp. 5951-5956
Author(s):  
Fu Jin Zhang ◽  
Yu Chun Ma ◽  
Hong Xu Wang ◽  
Qing Zhang
Keyword(s):  
Pattern Recognition ◽  
System Design ◽  
Similarity Measure ◽  
Irrigation System ◽  
Recognition Algorithm ◽  
Pattern Recognition Algorithm ◽  
Vague Sets ◽  
Recognition Algorithms ◽  
Definition Of ◽  
Irrigation System Design

Definition of conversion from a single value data to the Vague value data is given; two conversion formulas from a single value data to the Vague value data are given; a similarity measure formula between Vague sets are given; Vague pattern recognition algorithm is given. The algorithm is applied to irrigation system design, application examples show that theVague pattern recognition algorithms and formulas are all useful.

scholarly journals A review on performance analysis of data mining methods in IoT

2020 ◽  
Vol 7 (73) ◽  
pp. 193-200
Author(s):  
Ashutosh Kumar Dubey ◽  
Dimple Kapoor ◽  
Vijaita Kashyap
Keyword(s):  
Systematic Review ◽  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Performance Analysis ◽  
System Component ◽  
Different Types ◽  
Communication Capability ◽  
Mining Methods ◽  
And Performance

IoT is capable and helpful in interdisciplinary areas along with the convergence of multiple technologies and platforms. This study adheres the adaptation of data mining technologies along with big data and cloud computing with the IoT system with detailed discussion. This paper supports and provide systematic review and analysis based on the computational parameters and performance analysis. The analysis and discussion are based on the communication capability, system component communication, aspects of data mining, big data and cloud computing in IoT. Different types of transmission and communication barriers have also been discussed and analyze. Finally, based on the study and analysis a framework has been suggested for the smooth functioning of the IoT protocols.

scholarly journals A Triangular Similarity Measure for Case Retrieval in CBR and Its Application to an Agricultural Decision Support System

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4605 ◽  
Author(s):  
Zhai ◽  
Ortega ◽  
Castillejo ◽  
Beltran
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Similarity Measure ◽  
Support System ◽  
Similarity Measures ◽  
Experimental Result ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
And Performance ◽  
Case Based

Case-based reasoning has been a widely-used approach to assist humans in making decisions through four steps: retrieve, reuse, revise, and retain. Among these steps, case retrieval plays a significant role because the rest of processes cannot proceed without successfully identifying the most similar past case beforehand. Some popular methods such as angle-based and distance-based similarity measures have been well explored for case retrieval. However, these methods may match inaccurate cases under certain extreme circumstances. Thus, a triangular similarity measure is proposed to identify commonalities between cases, overcoming the drawbacks of angle-based and distance-based measures. For verifying the effectiveness and performance of the proposed measure, case-based reasoning was applied to an agricultural decision support system for pest management and 300 new cases were used for testing purposes. Once a new pest problem is reported, its attributes are compared with historical data by the proposed triangular similarity measure. Farmers can obtain quick decision support on managing pest problems by learning from the retrieved solution of the most similar past case. The experimental result shows that the proposed measure can retrieve the most similar case with an average accuracy of 91.99% and it outperforms the other measures in the aspects of accuracy and robustness.

Using the Similarity Measure Between Intuitionistic Fuzzy Sets for the Application on Pattern Recognitions

2018 ◽  
pp. 972-985
Author(s):  
Lixin Fan
Keyword(s):  
Pattern Recognition ◽  
Fuzzy Sets ◽  
Similarity Measure ◽  
Medical Diagnosis ◽  
Similarity Measures ◽  
Intuitionistic Fuzzy Sets ◽  
Distance Measures ◽  
Membership Degree ◽  
Intuitionistic Fuzzy ◽  
Definition Of

The measurement of uncertainty is an important topic for the theories dealing with uncertainty. The definition of similarity measure between two IFSs is one of the most interesting topics in IFSs theory. A similarity measure is defined to compare the information carried by IFSs. Many similarity measures have been proposed. A few of them come from the well-known distance measures. In this work, a new similarity measure between IFSs was proposed by the consideration of the information carried by the membership degree, the non-membership degree, and hesitancy degree in intuitionistic fuzzy sets (IFSs). To demonstrate the efficiency of the proposed similarity measure, various similarity measures between IFSs were compared with the proposed similarity measure between IFSs by numerical examples. The compared results demonstrated that the new similarity measure is reasonable and has stronger discrimination among them. Finally, the similarity measure was applied to pattern recognition and medical diagnosis. Two illustrative examples were provided to show the effectiveness of the pattern recognition and medical diagnosis.

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