Data Mining for ERW Welded Tube Scheduling Rules Based on Decision Tree

2011 ◽  
Vol 403-408 ◽  
pp. 1804-1807
Author(s):  
Ning Zhao ◽  
Shao Hua Dong ◽  
Qing Tian
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Arc Welding ◽  
Data Cleaning ◽  
Data Extraction ◽  
Electric Arc ◽  
Welded Tube ◽  
Electric Arc Welding ◽  
Decision Tree Method ◽  
Tree Method

In order to optimize electric- arc welding （ERW） welded tube scheduling , the paper introduces data cleaning, data extraction and transformation in detail and defines the datasets of sample attribute, which is based on analysis of production process of ERW welded tube. Furthermore, Decision-Tree method is adopted to achieve data mining and summarize scheduling rules which are validated by an example.

Animal Husbandry Decision Tree Data Mining Applications in Intelligent System

2014 ◽  
Vol 926-930 ◽  
pp. 2529-2532
Author(s):  
Shu Yan Wu ◽  
Hui Ling Wu ◽  
Tai Yu Liu ◽  
Xian Yu Cui
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Theoretical Basis ◽  
Animal Husbandry ◽  
Specific Work ◽  
Decision System ◽  
Intelligent Decision ◽  
Decision Tree Method ◽  
Tree Method ◽  
Intelligent Decision System

In this paper, the research based on decision tree-based methods can be customized to achieve a common data analysis system for animal husbandry intelligent decision support, conducted the following study: First, detail the current data mining techniques and animal husbandry areas where the decision tree method for various applications, purpose, content and methods. Secondly, the theoretical basis of the data mining system, basic theory and the theory of decision tree methods of animal husbandry intelligent decision system, which constitute the theoretical basis of the study. Third, given the detailed design of common animal husbandry intelligent decision system based decision tree method. Detailed descript of the specific work processes and systems function modules of the system on the line division. To complete the design of the database based on the completion of the system design.

scholarly journals Simulasi Prakiraan Dan Klasifikasi Hujan Wilayah Kota jakarta dengan Metode Decision Tree

2021 ◽  
Vol 4 (1) ◽  
pp. 27
Author(s):  
Arif Ega Prakosa ◽  
Ahmad Fawaid ◽  
Irkhas Nusantara ◽  
Faisal Amri ◽  
Aries Saifudin
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Natural Occurrence ◽  
Decision Tree Method ◽  
Tree Method

Rain is a natural occurrence that occurs in the hydrological and climatic circulation. Based on the ups and downs of the hydrological circulation, one of the sources of water is rain. rain is very useful in life, because rain can meet the needs of water for living creatures. However, rain can also cause floods. A flood tragedy can cause loss and casualties. Then our activities are checking the forecast simulation forecast and rain classification using the accurate decision tree method. We take forecast and classification data because the weather in DKI Jakarta is currently very difficult to predict. Next, we use a decision tree for data mining with a dataset spanning 5 years from 2011-2015. The problem here is the weather in DKI Jakarta which is very difficult to predict.

The Pruning Strategy of Decision Tree of SMT Assembling Quality Control Based on Data Mining

2011 ◽  
Vol 204-210 ◽  
pp. 704-707
Author(s):  
Hua Jiang ◽  
Tong Lai Liu ◽  
Han Lei He
Keyword(s):  
Data Mining ◽  
Quality Control ◽  
Decision Tree ◽  
Data Warehouse ◽  
Decision Support Model ◽  
Pruning Strategy ◽  
Effective Decision ◽  
Decision Tree Method ◽  
Effective Decision Making ◽  
Tree Method

Based on the establishment of data warehouse of the decision support model of the SMT assembling quality control, this paper relies its basis on the SLIQ algorithm of the decision tree method of data mining, improves the pruning strategy and conducts a useful exploration on how to form more effective decision-making basis of the quality control system of SMT assembling in order to improve the accuracy and predictability of decision analysis.

scholarly journals C4.5 Classification Data Mining for Inventory Control

2018 ◽  
Vol 7 (2.3) ◽  
pp. 68 ◽  
Author(s):  
Robbi Rahim ◽  
Ilka Zufria ◽  
Nuning Kurniasih ◽  
Muhammad Yasin Simargolang ◽  
Abdurrozzaq Hasibuan ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Tree ◽  
Large Data ◽  
Production Costs ◽  
Traffic Data ◽  
Tree Form ◽  
Minimal Error ◽  
Decision Tree Method ◽  
Tree Method

Data Mining is a process of exploring against large data to find patterns in decision making. One of the techniques in decision-making is classification. Classification is a technique in data mining by applying decision tree method to form data, algorithm C4.5 is algorithm that can be used to classify data in tree form. The system has been built that shows the results of good performance and minimal error in view of the system that is able to distinguish the anomaly traffic with normal traffic. Data mining inventory system applications can facilitate the control of inventory in the company to reduce production costs. 

scholarly journals Prediksi Mahasiswa Berpotensi Non Aktif Menggunakan Data Mining dalam Decision Tree dan Algoritma C4.5

2020 ◽  
pp. 23-29
Author(s):  
Nur Yanti Lumban Gaol
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Dropping Out ◽  
Mining Science ◽  
Prediction Rules ◽  
Tertiary Institutions ◽  
Student Data ◽  
C4.5 Algorithm ◽  
Decision Tree Method ◽  
Tree Method

Non-active students are students who do not attend the lecture process and do not pay tuition administration fees within two semesters or more. Reports on students who are not active will have an impact on the quantity of tertiary institutions. Students who are not registered in non-active students will potentially be expelled or dropped out. For this reason, this research was conducted to explore information on potentially non-active students by applying data mining science with the Decision Tree method and C4.5 algorithm. The tested data were sourced from Triguna Dharma Medan College of Information and Computer Management (STMIK). The results of the study get prediction rules for student data that are potentially non-active with a very good degree of accuracy. So this research can be used to avoid students dropping out unilaterally.

scholarly journals Prediksi Mahasiswa Berpotensi Non Aktif Menggunakan Data Mining dalam Decision Tree dan Algoritma C4.5

2019 ◽  
Vol 1 (4) ◽  
pp. 40-46
Author(s):  
Nur Yanti Lumban Gaol
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Dropping Out ◽  
Mining Science ◽  
Prediction Rules ◽  
Tertiary Institutions ◽  
Student Data ◽  
C4.5 Algorithm ◽  
Decision Tree Method ◽  
Tree Method

Non-active students are students who do not attend the lecture process and do not pay tuition administration fees within two semesters or more. Reports on students who are not active will have an impact on the quantity of tertiary institutions. Students who are not registered in non-active students will potentially be expelled or dropped out. For this reason, this research was conducted to explore information on potentially non-active students by applying data mining science with the Decision Tree method and C4.5 algorithm. The tested data were sourced from Triguna Dharma Medan College of Information and Computer Management (STMIK). The results of the study get prediction rules for student data that are potentially non-active with a very good degree of accuracy. So this research can be used to avoid students dropping out unilaterally.

Electric arc welding and surfacing in repair of rails of Kyiv underground

2018 ◽  
Vol 2018 (2) ◽  
pp. 30-33
Author(s):  
G.V. Kuzmenko ◽  
◽  
V.M. Taganovsky ◽  
V.L. Sidorenko ◽  
◽  
...  
Keyword(s):  
Arc Welding ◽  
Electric Arc ◽  
Electric Arc Welding

Rancang Bangun Aplikasi Sistem Pakar untuk Menangani Masalah Kecantikan pada Wajah Menggunakan Metode Decision Tree

2014 ◽  
Vol 6 (1) ◽  
pp. 9-14
Author(s):  
Stefanie Sirapanji ◽  
Seng Hansun
Keyword(s):  
Decision Tree ◽  
Uneven Distribution ◽  
Dry Skin ◽  
Average Accuracy ◽  
Accuracy Level ◽  
Index Terms ◽  
Decision Tree Method ◽  
Tree Method

Beauty is a precious asset for everyone. Everyone wants to have a healthy face. Unfortunately, there are always those problems that pops out on its own. For example, acnes, freckles, wrinkles, dull, oily and dry skin. Therefore, nowadays, there are a lot of beauty clinics available to help those who wants to solve their beauty troubles. But, not everyone can enjoy the facilities of those beauty clinics, for example those in the suburbs. The uneven distribution of doctors and the expensive cost of treatments are some of the reasons. In this research, the system that could help the patients to find the solution of their beauty problems is built. The decision tree method is used to take decision based on the shown schematic. Based on the system’s experiment, the average accuracy level hits 100%. Index Terms–Acnes, Decision Tree, Dry Skin, Dull, Facial Problems, Freckles, Wrinkles, Oily Skin, Eexpert System.

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