A New Associative Classification Algorithm for Predicting Groundwater Locations

2018 ◽  
Vol 17 (04) ◽  
pp. 1850043
Author(s):  
Faisal Aburub ◽  
Wa’el Hadi
Keyword(s):  
Data Mining ◽  
Groundwater Exploration ◽  
Classification Algorithms ◽  
Associative Classification ◽  
Detection Problem ◽  
Classification Rate ◽  
Data Mining Algorithms ◽  
New Locations ◽  
Using Data ◽  
Mining Algorithms

In this paper, we study the problem of predicting new locations of groundwater in Jordan through the application of a proposed new method, Groundwater Prediction using Associative Classification (GwPAC). We identify features that differentiate locations of groundwater wells according to whether or not they contain water. In addition, we survey intelligent-based methods related to groundwater exploration and management. Three experimental analyses were conducted with the objective to evaluate the capability of data mining algorithms using real groundwater data from the Ministry of Water and Irrigation. In the first experiment, we investigated the performance of GwPAC against three well-known associative classification algorithms, namely CBA, CMAR and FACA. Furthermore, three rule-based algorithms — C4.5, Random Forest and PBC4cip — were investigated in the second experiment; further, so as to generalise the capability of using data mining for solving the groundwater detection problem, four benchmark algorithms — SVMs, NB, KNN and ANNs — were evaluated in the third experiment. From all the experiments, the results indicated that all considered data mining algorithms predict locations of groundwater with acceptable classification rate (all classification accuracies [Formula: see text]%), and can be useful methods when seeking to address the problem of exploring new groundwater locations.

scholarly journals A data mining analysis of COVID-19 cases in states of United States of America

2022 ◽  
Vol 12 (2) ◽  
pp. 1754
Author(s):  
Özerk Yavuz
Keyword(s):  
Data Mining ◽  
Mean Squared Error ◽  
Negative Effects ◽  
Classification Rate ◽  
Data Mining Algorithms ◽  
Squared Error ◽  
Using Data ◽  
Algorithmic Technique ◽  
Mining Algorithms ◽  
Epidemic Diseases

Epidemic diseases can be extremely dangerous with its hazarding influences. They may have negative effects on economies, businesses, environment, humans, and workforce. In this paper, some of the factors that are interrelated with COVID-19 pandemic have been examined using data mining methodologies and approaches. As a result of the analysis some rules and insights have been discovered and performances of the data mining algorithms have been evaluated. According to the analysis results, JRip algorithmic technique had the most correct classification rate and the lowest root mean squared error (RMSE). Considering classification rate and RMSE measure, JRip can be considered as an effective method in understanding factors that are related with corona virus caused deaths.

scholarly journals Simple Correlation Between Weather and COVID-19 Pandemic Using Data Mining Algorithms

2020 ◽  
Vol 982 ◽  
pp. 012015
Author(s):  
Ari Fadli ◽  
Azis Wisnu Widhi Nugraha ◽  
Muhammad Syaiful Aliim ◽  
Acep Taryana ◽  
Yogiek Indra Kurniawan ◽  
...  
Keyword(s):  
Data Mining ◽  
Simple Correlation ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Mining Algorithms

scholarly journals Osteoporosis Risk Prediction Using Data Mining Algorithms

2020 ◽  
Author(s):  
Efat Jabarpour ◽  
Amin Abedini ◽  
Abbasali Keshtkar
Keyword(s):  
Data Mining ◽  
Personal Information ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Data Mining Algorithms ◽  
Industry Standard ◽  
Disease Information ◽  
Increased Risk ◽  
Using Data ◽  
Mining Algorithms

Introduction: Osteoporosis is a disease that reduces bone density and loses the quality of bone microstructure leading to an increased risk of fractures. It is one of the major causes of inability and death in elderly people. The current study aims at determining the factors influencing the incidence of osteoporosis and providing a predictive model for the disease diagnosis to increase the diagnostic speed and reduce diagnostic costs. Methods: An Individual's data including personal information, lifestyle, and disease information were reviewed. A new model has been presented based on the Cross-Industry Standard Process CRISP methodology. Besides, Support Vector Machine (SVM) and Bayes methods (Tree Augmented Naïve Bayes (TAN)) and Clementine12 have been used as data mining tools. Results: Some features have been detected to affect this disease. The rules have been extracted that can be used as a pattern for the prediction of the patients' status. Classification precision was calculated to be 88.39% for SVM, and 91.29% for  (TAN) when the precision of  TAN  is higher comparing to other methods. Conclusion: The most effective factors concerning osteoporosis are detected and can be used for a new sample with defined characteristics to predict the possibility of osteoporosis in a person.  

Analysis of the Samples with an Unknown Matrix Using Data Mining Algorithms

2017 ◽  
Vol 53 (14) ◽  
pp. 1454-1457
Author(s):  
E. I. Molchanova ◽  
E. N. Korzhova ◽  
T. V. Stepanova ◽  
V. V. Kuz’min
Keyword(s):  
Data Mining ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Mining Algorithms
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