Applications of Data Mining Algorithm in Equipment Fault Diagnosis

2014 ◽  
Vol 644-650 ◽  
pp. 2551-2555
Author(s):  
Rong Xiang Li ◽  
Zeng Lei Zhang ◽  
Yun Liu ◽  
Shan Chao Tu
Keyword(s):  
Data Mining ◽  
Fault Diagnosis ◽  
Decision Tree ◽  
Decision Trees ◽  
Data Mining Algorithm ◽  
Basic Principles ◽  
Mining Algorithm ◽  
Id3 Algorithm ◽  
Better Than ◽  
Improved Algorithm

The Basic Principles of Data mining Decision-tree ID3 is opened out. The main deficiencies are analysed. An improved algorithm based on the ID3 is calculated. For fault diagnosis of engine exemple, traditional ID3 algorithm and the improved algorithm are applied to estimate the fault diagnosis of engine separately. Decision Trees of traditional ID3 algorithm and the improved algorithm are construct. Experiment result display the accuracy of improved algorithm is better than traditional ID3. The improved algorithm is more fit to applied to the equipment fault diagnosis.

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 Big Data Technology Application in Mechanical Intelligent Fault Diagnosis

2021 ◽  
Vol 2066 (1) ◽  
pp. 012064
Author(s):  
Huiteng Cao
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Big Data ◽  
Fault Diagnosis ◽  
Machining Process ◽  
Mechanical Equipment ◽  
Data Mining Algorithm ◽  
Intelligent Fault Diagnosis ◽  
Mining Algorithm ◽  
Big Data Technology

Abstract With the rapid implementation of made in China 2025 plan and the rapid development and application of information technology such as artificial intelligence, big data technology, industrial Internet of things and 5G, information technology has been integrated into every link of the whole life management cycle of mechanical products, such as tool condition detection and mechanical fault diagnosis in machining process. Based on this, the purpose of this study is to study the application of big data technology in mechanical intelligent fault diagnosis. In the process of this study, the decision number algorithm and data mining algorithm are used to study the experiment, and some mechanical faults in the past are analyzed and studied. Summary of the experimental results show that the use of decision number algorithm and data mining algorithm in the experiment has achieved good results, through these methods and big data technology, we can quickly diagnose the fault of mechanical equipment, accurately locate the fault location of mechanical equipment. Mechanical intelligent fault diagnosis based on big data technology can improve the efficiency of fault diagnosis, reduce enterprise costs and improve economic performance.

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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