Application of Decision Tree as a Data mining Tool in a Manufacturing System

2011 ◽  
pp. 117-136
Author(s):  
S.A. Oke
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Tree ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Research Activity ◽  
Data Mining Tool ◽  
Mining Tool ◽  
Classification Prediction ◽  
Effective Decision Making

This work demonstrates the application of decision tree, a data mining tool, in the manufacturing system. Data mining has the capability for classification, prediction, estimation, and pattern recognition by using manufacturing databases. Databases of manufacturing systems contain significant information for decision making, which could be properly revealed with the application of appropriate data mining techniques. Decision trees are employed for identifying valuable information in manufacturing databases. Practically, industrial managers would be able to make better use of manufacturing data at little or no extra investment in data manipulation cost. The work shows that it is valuable for managers to mine data for better and more effective decision making. This work is therefore new in that it is the first time that proper documentation would be made in the direction of the current research activity.

Application of Decision Tree as a Data Mining Tool in a Manufacturing System

2011 ◽  
pp. 234-251
Author(s):  
S. A. Oke
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Tree ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Research Activity ◽  
Data Mining Tool ◽  
Mining Tool ◽  
Classification Prediction ◽  
Effective Decision Making

This work demonstrates the application of decision tree, a data mining tool, in the manufacturing system. Data mining has the capability for classification, prediction, estimation, and pattern recognition by using manufacturing databases. Databases of manufacturing systems contain significant information for decision making, which could be properly revealed with the application of appropriate data mining techniques. Decision trees are employed for identifying valuable information in manufacturing databases. Practically, industrial managers would be able to make better use of manufacturing data at little or no extra investment in data manipulation cost. The work shows that it is valuable for managers to mine data for better and more effective decision making. This work is therefore new in that it is the first time that proper documentation would be made in the direction of the current research activity.

Application of Decision Tree as a Data Mining Tool in a Manufacturing System

2009 ◽  
pp. 940-955
Author(s):  
S. A. Oke
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Tree ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Research Activity ◽  
Data Mining Tool ◽  
Mining Tool ◽  
Classification Prediction ◽  
Effective Decision Making

This work demonstrates the application of decision tree, a data mining tool, in the manufacturing system. Data mining has the capability for classification, prediction, estimation, and pattern recognition by using manufacturing databases. Databases of manufacturing systems contain significant information for decision making, which could be properly revealed with the application of appropriate data mining techniques. Decision trees are employed for identifying valuable information in manufacturing databases. Practically, industrial managers would be able to make better use of manufacturing data at little or no extra investment in data manipulation cost. The work shows that it is valuable for managers to mine data for better and more effective decision making. This work is therefore new in that it is the first time that proper documentation would be made in the direction of the current research activity.

scholarly journals Komparasi Algoritma Nonparametrik untuk Klasifikasi Citra Wajah Berdasarkan Suku di Indonesia

2020 ◽  
Vol 6 (3) ◽  
pp. 337
Author(s):  
Seno Hartono ◽  
Anggi Perwitasari ◽  
Herry Sujaini
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Mining Tool ◽  
Mining Tool

Klasifikasi merupakan metode data mining yang berfungsi untuk mengatur dan mengkategorikan data pada kelas yang berbeda-beda. Penelitian ini bertujuan untuk membandingkan dan menentukan algoritma nonparametrik terbaik dalam pengklasifikasian citra wajah. Dalam proses pengklasifikasian, penelitian ini menggunakan algoritma klasifikasi nonparametrik yaitu k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Decision Tree, dan AdaBoost Untuk mengklasifikasikan citra wajah penduduk Indonesia yang berasal dari suku Batak, Dayak, Jawa, Melayu, dan Tionghoa. Penelitian ini menggunakan Orange Data Mining Tool sebagai alat bantu untuk melakukan proses data mining. Dari hasil pengklasifikasian dengan menerapkan algoritma k-Nearest Neigbor, Support Vector Machine, Decision Tree, dan AdaBoost, SVM memberikan nilai akurasi yang lebih baik dibanding algoritma lainnya. Rata-rata nilai precision keempat algoritma tersebut berturut-turut adalah Support Vector Machine 37.5%, diikuti oleh algoritma k-Nearest Neighbor 31.55%, AdaBoost 30.25%, dan untuk Decision Tree 29.75%.

Comparative Study of Data Mining Classifiers for Students’ Academic Performance

2020 ◽  
Vol 17 (9) ◽  
pp. 4548-4552
Author(s):  
Vikas Rattan ◽  
Ruchi Mittal ◽  
Varun Malik
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Decision Tree ◽  
Comparative Study ◽  
Student Performance ◽  
Nearest Neighbor ◽  
Educational Institutions ◽  
Competitive Edge ◽  
Data Mining Tool ◽  
Mining Tool

Tremendous growth of educational institutions forced educational institutes to adopt data mining techniques to bring out important and yet unknown facts from educational data to have a competitive edge over their counterparts. In this paper, student performance dataset comprises of 131 records is taken from UCI repository and data mining tool Orange is used to study the comparative analyses of accuracy for classifying the performance of student in graduation using four classifiers namely random forest, k nearest neighbor (KNN), decision tree and naïve bayes. The result shows that decision tree accuracy is highest among all other classifier

scholarly journals Using Decision Tree Classifier for Analyzing Students’ Activities

2013 ◽  
Vol 6 (2) ◽  
Author(s):  
Snježana Milinković ◽  
Mirjana Maksimović
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Decision Tree ◽  
Electrical Engineering ◽  
Decision Tree Classifier ◽  
Final Exam ◽  
Data Mining Tool ◽  
Tree Classifier ◽  
Mining Tool

In this paper students’ activities data analysis in the course Introduction to programming at Faculty of Electrical Engineering in East Sarajevo is performed. Using the data that are stored in the Moodle database combined with manually collected data, the model was developed to predict students’ performance in successfully passing the final exam. The goal was to identify variables that could help teachers in predicting students’ performance and making specific recommendations for improving individual activities that could directly influence final exam successful passing. The model was created using decision tree classifier and experiments were performed using the WEKA data mining tool. The effect of input attributes on the model performances was analyzed and applying appropriate techniques a higher accuracy of the generated model was achieved.

scholarly journals Identification of Models-Decision Tree and Random Forest Classifier using Rattle on Diabetes Disease

2019 ◽  
Vol 8 (9S2) ◽  
pp. 172-176
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Decision Tree ◽  
Forest Tree ◽  
Medical Expert ◽  
Data Mining Tool ◽  
Random Forest Tree ◽  
Mining Tool ◽  
The Many ◽  
The Given

Diabetes is the disease which is growing now a days in human body and there are a number of patient who are suffering by this diabetes in the world. The data related to medical area is very huge which is related to the many disease. So the first thing is that we have to choose a mining tool which give best result for the given databases. Because, this medical data is statistical and most of the researchers using this type of data. Data mining tool is used for the extracting better result in accuracy for the diabetes data base. By the data mining techniques the medical expert and researchers analyze the result and provide the best treatment for this disease. In this paper we are using diabetes data and apply it on the Rattle, an open source tool of data mining and perform two classification methods decision tree and random forest tree for classify the data and show that which classification algorithm is best for diabetes datase

A data mining tool for learning from manufacturing systems

1997 ◽  
Vol 33 (1-2) ◽  
pp. 27-30 ◽  
Author(s):  
David A. Koonce ◽  
Cheng-Hung Fang ◽  
Shi-Chi Tsai
Keyword(s):  
Data Mining ◽  
Manufacturing Systems ◽  
Data Mining Tool ◽  
Mining Tool
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