Predictive Data Mining Techniques for Production Optimization

2004 ◽  
Author(s):  
G. Zangl ◽  
C.P. Oberwinkler
Keyword(s):  
Data Mining ◽  
Production Optimization ◽  
Data Mining Techniques ◽  
Predictive Data Mining

A comparative analysis of predictive data mining techniques

2009 ◽  
Vol 1 (2) ◽  
pp. 150 ◽  
Author(s):  
Xueping Li ◽  
Godswill Chukwugozie Nsofor ◽  
Laigang Song
Keyword(s):  
Data Mining ◽  
Comparative Analysis ◽  
Data Mining Techniques ◽  
Predictive Data Mining

scholarly journals Enhancing the Quality Education using Predictive and Descriptive Data Mining Model

2019 ◽  
Vol 8 (3) ◽  
pp. 6843-6847
Keyword(s):  
Data Mining ◽  
Student Performance ◽  
Educational Data Mining ◽  
Strong Relationship ◽  
Data Mining Techniques ◽  
Huge Data ◽  
Student’S Performance ◽  
Predictive Data Mining ◽  
Mining Model ◽  
The Given

Data mining is the trending field used to get relevant knowledge from the database given. This technique consists of subfield called educational data mining is the emerging area used to extract the hidden patterns from the huge data with the help of tools techniques developed by the researchers of the educational data mining. The purpose of extracting patterns from the educational database is to improve the quality of education can be provided to the students for their better feature. The patterns are extracted by using the existing data mining techniques to enhance student performance. Educational data mining techniques such as classification, regression, clustering are available in the field. Classification is defined as the technique used to categorize the data based on the given label and constraints. In this paper, the algorithms like naves Bayes, Random Forest and J48 algorithms used to classify the data instances under the given labels using the constraints given., the classification algorithms like naves Bayes shows best performance accuracy with the given student dataset. Clustering and apriori rule have a strong relationship in student performance. In this paper, predictive data mining used to predict the student's performance to enhance the study level of the students in the organization.

A Comparative Study on Medical Diagnosis Using Predictive Data Mining

2014 ◽  
pp. 327-360 ◽  
Author(s):  
Seyed Jalaleddin Mousavirad ◽  
Hossein Ebrahimpour-Komleh
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Medical Diagnosis ◽  
Medical Data ◽  
Support Vector ◽  
Medical Data Mining ◽  
Data Mining Techniques ◽  
Predictive Data Mining

Medical diagnosis is a most important problem in medical data mining. The possible errors of a physician can reduce with the help of data mining techniques. The goal of this chapter is to analyze and compare predictive data mining techniques in the medical diagnosis. To this purpose, various data mining techniques such as decision tree, neural networks, support vector machine, and lazy modelling are considered. Results show data mining techniques can considerably help a physician.

scholarly journals Predictive Data Mining Techniques for Fault Diagnosis of Electric Equipment: A Review

2020 ◽  
Vol 10 (3) ◽  
pp. 950 ◽  
Author(s):  
Arantxa Contreras-Valdes ◽  
Juan P. Amezquita-Sanchez ◽  
David Granados-Lieberman ◽  
Martin Valtierra-Rodriguez
Keyword(s):  
Data Mining ◽  
Fault Diagnosis ◽  
Scientific Field ◽  
Data Mining Techniques ◽  
Large Databases ◽  
The World ◽  
Electric Equipment ◽  
Classification And Regression ◽  
Predictive Data Mining ◽  
Year 2000

Data mining is a technological and scientific field that, over the years, has been gaining more importance in many areas, attracting scientists, developers, and researchers around the world. The reason for this enthusiasm derives from the remarkable benefits of its usefulness, such as the exploitation of large databases and the use of the information extracted from them in an intelligent way through the analysis and discovery of knowledge. This document provides a review of the predictive data mining techniques used for the diagnosis and detection of faults in electric equipment, which constitutes the pillar of any industrialized country. Starting from the year 2000 to the present, a revision of the methods used in the tasks of classification and regression for the diagnosis of electric equipment is carried out. Current research on data mining techniques is also listed and discussed according to the results obtained by different authors.

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