Study of data mining algorithm based on decision tree

2010 ◽  
Author(s):  
Linna Li ◽  
Xuemin Zhang
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Data Mining Algorithm ◽  
Mining Algorithm

scholarly journals Analysis of sales levels of pharmaceutical products by using data mining algorithm C45

2021 ◽  
Vol 22 (1) ◽  
pp. 476
Author(s):  
Rini Sovia ◽  
Abulwafa Muhammad ◽  
Syafri Arlis ◽  
Guslendra Guslendra ◽  
Sarjon Defit
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Data Processing ◽  
Medicinal Product ◽  
Pharmaceutical Products ◽  
Data Mining Algorithm ◽  
Sales Data ◽  
Great Demand ◽  
Mining Algorithm ◽  
Using Data

<p>This research was conducted to analyze the level of sales of pharmaceutical products at a Pharmacy. This is done to find out the types of products that have high and low sales levels. This study uses the C45 Data Mining Algorithm concept that will produce a conclusion on the prediction of sales of pharmaceutical products through data processing obtained from sales transactions at pharmacies. This C45 algorithm will form a decision tree that provides users with knowledge about products that are in great demand by consumers based on sales data and predetermined variables. The final result of the C45 algorithm produces a number of rules that can identify the inheritance of a type of medicinal product. C45 algorithm is able to produce 20 types of categories that will be labeled goals based on the number of pharmaceutical products, since it can be concluded that C45 successfully defines 55% of the existing objective categories.</p>

scholarly journals Decision Tree: A Predictive Modeling Tool Used in Cloud Trust Prediction

2019 ◽  
Vol 8 (6) ◽  
pp. 4779-4783
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Predictive Modeling ◽  
Cloud Provider ◽  
Data Mining Algorithm ◽  
Trust Value ◽  
The Public ◽  
Mining Algorithm ◽  
Current Scenario ◽  
Reliable Basis

Trust is one of the important challenges faced by the cloud industry. Ever increasing data theft cases are contributing in worsening this issue. Regarding trust, author has a perception that this challenge can be handled to some extend if consumer can evaluate “Trust Value “ of the provider or can predict the same on some reliable basis. Current research is using predictive modeling for predicting trustworthiness of cloud provider. This paper is an attempt to utilize the data mining algorithm for predictive modeling. Decision Tree, a supervised data mining algorithm has been used in the current work for making predictions. Certification attainment criteria as prime basis for trust evaluation. In current scenario, data mining algorithm will classify providers in category of low, medium and high category of trust on the basis of information displayed on the public domain

scholarly journals Data mining algorithm for development of a predictive model for mitigating loan risk in Nigerian banks

2021 ◽  
Vol 25 (9) ◽  
pp. 1613-1616
Author(s):  
O.B. Alaba ◽  
E.O. Taiwo ◽  
O.A. Abass
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Predictive Model ◽  
Risk Model ◽  
Data Mining Algorithm ◽  
Tree Model ◽  
Mining Algorithm ◽  
Loan Risk ◽  
Structured Questionnaire ◽  
J48 Decision Tree

The focus of this paper is on the development of data mining algorithm for developing of predictive loan risk model for Nigerian banks. The model classifies and predicts the risk involved in granting loans to customers as either good or bad loan by collecting data based on J48 decision tree, BayesNet and Naïve Bayes algorithms for a period of ten (10) years (2010 2019) from using structured questionnaire. The formulation and simulation of the predictive model were carried out using Waikato Environment for Knowledge Analysis (WEKA) software. The performance of the three algorithms for predicting loan risk was done based on accuracy and error rate metrics. The study revealed that J48 decision tree model is the most efficient of all the three models.

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