A Decision Tree Model to Analyze the Characteristics of the Elderly with ADL Limitation Using Data Mining

2012 ◽  
pp. 508-515
Author(s):  
Myonghwa Park ◽  
Sungjin Kim
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
The Elderly ◽  
Decision Tree Model ◽  
Tree Model ◽  
Using Data

An Educational Data Mining Application by Using Multiple Intelligences

2020 ◽  
pp. 93-110
Author(s):  
Esra Aksoy ◽  
Serkan Narli ◽  
Mehmet Akif Aksoy
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Learning Styles ◽  
Multiple Intelligences ◽  
Gifted Students ◽  
Personality Types ◽  
Decision Tree Model ◽  
Tree Model ◽  
Educational Domain ◽  
Using Data

The aim of this chapter is to illustrate both uses of data mining methods and the way of these methods can be applied in education by using students' multiple intelligences. Data mining is a data analysis methodology that has been successfully used in different areas including the educational domain. In this context, in this study, an application of EDM will be illustrated by using multiple intelligence and some other variables (e.g., learning styles and personality types). The decision tree model was implemented using students' learning styles, multiple intelligences, and personality types to identify gifted students. The sample size was 735 middle school students. The constructed decision tree model with 70% validity revealed that examination of mathematically gifted students using data mining techniques may be possible if specific characteristics are included.

Mathematical modelling of evapotranspiration by using remote sensing and data mining

2021 ◽  
Author(s):  
Lamya Neissi ◽  
Mona Golabi ◽  
Mohammad Albaji ◽  
Abd Ali Naseri
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Decision Tree ◽  
Specific Area ◽  
Decision Tree Model ◽  
Tree Model ◽  
Water Index ◽  
Model Builder ◽  
Using Data ◽  
Extended Area

Abstract Precise evaluation of evapotranspiration in an extended area is crucial for water requirement. By using remote sensing evapotranspiration algorithms, many climatological variables are needed. In case of using climatological variable measurements, many climatic stations must be established in that specific area. By using data mining method integrated with remote sensing, evapotranspiration can be calculated with high accuracy. A physical-based SEBAL evapotranspiration algorithm was modeled by GIS model builder for ET calculations. Albedo, emissivity, and Normalized Difference Water Index (NDWI) were considered as M5 decision tree model inputs. Evapotranspiration was evaluated for 3 April 2020 to 17 September 2020 and the equations were extracted in the M5 decision tree model and these equations were modeled in GIS by using python scripts for 3 April 2020 to 17 September 2020. The results make clear that the mathematical decision tree model can estimate the evapotranspiration gained by physical-based SEBAL algorithm in high accurately.

scholarly journals Penerapan Algoritma C4.5 Dalam Memprediksi Ketersediaan Uang Pada Mesin ATM

2021 ◽  
Vol 5 (2) ◽  
pp. 556
Author(s):  
Firman Syahputra ◽  
Hartono Hartono ◽  
Rika Rosnelly
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Travel Time ◽  
Tree Model ◽  
Optimal Service ◽  
C4.5 Algorithm ◽  
Cash Transaction ◽  
Auc Value ◽  
Using Data ◽  
Balance Variable

This study aims to provide an evaluation of the availability of money in ATM machines using data mining. Data mining with the C4.5 algorithm is used to predict cash demand or total cash withdrawals at ATMs. To determine the need for ATM cash based on cash transaction data. It is hoped that this forecasting can help the monitoring department in making decisions about the money requirements that must be allocated to each ATM machine. The results of this study are expected to assist the ATM management unit in optimizing and monitoring the availability of money at an ATM machine for cash needs, so that it can provide optimal service to customers. Algortima C4.5 is an algorithm that is able to form a decision tree, where the decision tree will then generate new knowledge. The results of the test matched the data on the availability of money at the ATM machine. The results of implementing the C4.5 method on the availability of money at the ATM machine are seen from the travel time to the ATM location and also the remaining balance in the machine. The resulting decision tree model is to make the balance variable as the root, then the travel time as a branch at Level 1 with the variables fast, medium, long, and the bank becomes a branch at the last level (Level 2). Then the C4.5 algorithm was tested using the K-Fold Cross validation method with the value of fold = 10, it can be seen that the accuracy rate is 85%, the Precision value is 80% and the Recall value is 66.67%. While the AUC (Area Under Curve) value is 0.833, this shows that if the AUC value approaches the value 1, the accuracy level is getting better

scholarly journals Customer Retention in Banking Sector using Decision Tree-Neuro Based System

2020 ◽  
Vol 9 (3) ◽  
pp. 481-485
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Tree ◽  
Banking Sector ◽  
Learning Algorithm ◽  
Decision Tree Model ◽  
Tree Model ◽  
Data Mining Techniques ◽  
Individual Decisions ◽  
Bank Marketing

All the bank marketing campaigns mostly deals with large amount of data. when they need to deal with huge electronic data of customers, then it is very difficult to analyze the data manually or by human analyst. Here comes the picture of data mining techniques to deal with the large amount of data and to come up with useful data which helps in decision making process. All the data mining techniques helps in analyzing the data. some of the techniques that can be used for this bank marketing campaigns are naive bayes, logistics regression technique and Decision tree model technique etc. among all these techniques decision Tree model gives the best solution in analyzing the human decisions. Artificial neural networks is a learning algorithm which learns from multiple individual decisions and their judgements, thus aggregates and generalizes the customers decision making knowledge.

Building a Decision Tree Model for Campus Information Score Based on the Algorithm C5.0

2015 ◽  
Vol 719-720 ◽  
pp. 805-811 ◽  
Author(s):  
Cong Lin Ran ◽  
Xiao Jing Wang
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Decision Tree Model ◽  
Utilization Rate ◽  
Network Technology ◽  
Tree Model ◽  
Weight Value ◽  
Information Score ◽  
Almost All ◽  
The Web

Network technology accelerates the development of educational information, campus portal building is considered as an important part of it in every university, almost all information of teaching and research appeared on the web. Meanwhile, the utilization rate of some websites was lower in university, information was updated slowly, information classifications were complex and not standardized on a platform. They didn’t emphasis on using and sharing but building and developing, and this phenomenon was widespread. So the paper proposed a decision tree model for score sorting information based on C5.0 algorithm, setting up a statistical model for data mining by adding a line weight value for portal information. Finally, the results verify the correctness and science of the model by giving an example.

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