Heuristic extraction of fuzzy classification rules using data mining techniques: an empirical study on benchmark data sets

2005 ◽  
Author(s):  
H. Ishibuchi ◽  
T. Yamamoto
Keyword(s):  
Data Mining ◽  
Empirical Study ◽  
Fuzzy Classification ◽  
Data Sets ◽  
Classification Rules ◽  
Data Mining Techniques ◽  
Benchmark Data ◽  
Using Data

Finding fuzzy classification rules using data mining techniques

2003 ◽  
Vol 24 (1-3) ◽  
pp. 509-519 ◽  
Author(s):  
Yi-Chung Hu ◽  
Ruey-Shun Chen ◽  
Gwo-Hshiung Tzeng
Keyword(s):  
Data Mining ◽  
Fuzzy Classification ◽  
Classification Rules ◽  
Data Mining Techniques ◽  
Using Data

scholarly journals Student Performance Predictions Using Knowledge Discovery Database and Data Mining, DPU Students Records as Sample

2021 ◽  
Vol 10 (3) ◽  
pp. 121-127
Author(s):  
Bareen Haval ◽  
Karwan Jameel Abdulrahman ◽  
Araz Rajab
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Student Performance ◽  
Educational Data Mining ◽  
Data Sets ◽  
Decision Tree Classifier ◽  
Data Mining Techniques ◽  
Academic History ◽  
Tree Classifier ◽  
Using Data

This article presents the results of connecting an educational data mining techniques to the academic performance of students. Three classification models (Decision Tree, Random Forest and Deep Learning) have been developed to analyze data sets and predict the performance of students. The projected submission of the three classificatory was calculated and matched. The academic history and data of the students from the Office of the Registrar were used to train the models. Our analysis aims to evaluate the results of students using various variables such as the student's grade. Data from (221) students with (9) different attributes were used. The results of this study are very important, provide a better understanding of student success assessments and stress the importance of data mining in education. The main purpose of this study is to show the student successful forecast using data mining techniques to improve academic programs. The results of this research indicate that the Decision Tree classifier overtakes two other classifiers by achieving a total prediction accuracy of 97%.

scholarly journals A Study: Breast Cancer Prediction Using Data Mining Techniques

2019 ◽  
Vol 8 (S2) ◽  
pp. 52-56
Author(s):  
B. Gousbi ◽  
A. R. Mohamed Shanavas
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Vital Role ◽  
Data Sets ◽  
Data Mining Techniques ◽  
Cancer Prediction ◽  
Prevention Of Cancer ◽  
Genetic And Environmental Factors ◽  
Using Data ◽  
Potential Cancer

Data mining is the extraction of unseen predictive info from huge databases, is the process of arranging through enormous data sets to recognize patterns and create relationships to resolve the problems through data analysis. Cancer is one of the primary reasons of death wide-reaching. Timely detection and prevention of cancer plays a very vital role in decreasing deaths affected by cancer. Identification of genetic and environmental factors is very significant in emerging novel methods to identify and avert cancer. Many researchers’ use data mining techniques like clustering, classification and prediction find potential cancer patients. This paper focuses on a breast cancer prediction system built on data mining techniques. With the help of this system, people can guess the possibility of the breast cancer in the former stage itself.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

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