Determination of Voting Tendencies in Turkey through Data Mining Algorithms

2017 ◽  
Vol 9 (1) ◽  
pp. 50-58
Author(s):  
Ali Bayır ◽  
Sebnem Ozdemir ◽  
Sevinç Gülseçen
Keyword(s):  
Data Mining ◽  
Economic Status ◽  
Educational Status ◽  
Data Set ◽  
Data Mining Algorithms ◽  
C4.5 Decision Tree ◽  
Survey Results ◽  
Mining Methods ◽  
Using Data ◽  
Mining Algorithms

Political elections can be defined as systems that contain political tendencies and voters' perceptions and preferences. The outputs of those systems are formed by specific attributes of individuals such as age, gender, occupancy, educational status, socio-economic status, religious belief, etc. Those attributes can create a data set, which contains hidden information and undiscovered patterns that can be revealed by using data mining methods and techniques. The main purpose of this study is to define voting tendencies in politics by using some of data mining methods. According to that purpose, the survey results, which were prepared and applied before 2011 elections of Turkey by KONDA Research and Consultancy Company, were used as raw data set. After Preprocessing of data, models were generated via data mining algorithms, such as Gini, C4.5 Decision Tree, Naive Bayes and Random Forest. Because of increasing popularity and flexibility in analyzing process, R language and Rstudio environment were used.

scholarly journals Analisis Algoritma Klasifikasi C 4.5 Untuk Memprediksi Keberhasilan Immunotherapy Pada Penyakit Kutil

2019 ◽  
Vol 5 (2) ◽  
pp. 155-160 ◽  
Author(s):  
Ady Hermawan ◽  
Ardi Ramadhan Sukma ◽  
Riqardi Halfis
Keyword(s):  
Data Mining ◽  
Large Data ◽  
Wart Disease ◽  
Clinical Prediction ◽  
Accuracy Rate ◽  
Data Mining Algorithms ◽  
Mining Methods ◽  
Using Data ◽  
Mining Algorithms ◽  
Human Papiloma Virus

Maintaining skin health is one thing that is also needed. Not only health from inside, health from the outside must also be considered. There are so many skin problems that arise in the human body. Wart disease is characterized by small bumps on the surface of the skin which are generally caused by the Human Papiloma Virus (HPV) virus. One technique for treating wart disease is immunotherapy, this method is a treatment by increasing the immune system to deal with wart disease. Clinical predictions are growing very rapidly by adopting computer science and information technology in managing health and drug data, this clinical prediction can be produced from processing using data mining methods. Data mining is a popular method used to explore patterns or knowledge from large data stacks. C 4.5 algorithm which is one of the decision tree induction algorithms is also a method of data mining algorithms used to classify. This study aims to predict the success rate of immunotherapy treatment methods on wart disease with algorithm C 4.5 using RapidMiner. From the study it was known that the accuracy rate for processing immunotherapy data on wart disease to predict its success using the C 4.5 algorithm of 74.07%.

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.  

A Survey of Feature Selection Techniques

2011 ◽  
pp. 1888-1895 ◽  
Author(s):  
Barak Chizi ◽  
Lior Rokach ◽  
Oded Maimon
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Mining Method ◽  
Data Set ◽  
Data Mining Method ◽  
Data Mining Algorithms ◽  
Wrapper Approach ◽  
Computationally Intensive ◽  
Filter Approach ◽  
Mining Algorithms

Dimensionality (i.e., the number of data set attributes or groups of attributes) constitutes a serious obstacle to the efficiency of most data mining algorithms (Maimon and Last, 2000). The main reason for this is that data mining algorithms are computationally intensive. This obstacle is sometimes known as the “curse of dimensionality” (Bellman, 1961). The objective of Feature Selection is to identify features in the data-set as important, and discard any other feature as irrelevant and redundant information. Since Feature Selection reduces the dimensionality of the data, data mining algorithms can be operated faster and more effectively by using Feature Selection. In some cases, as a result of feature selection, the performance of the data mining method can be improved. The reason for that is mainly a more compact, easily interpreted representation of the target concept. The filter approach (Kohavi , 1995; Kohavi and John ,1996) operates independently of the data mining method employed subsequently -- undesirable features are filtered out of the data before learning begins. These algorithms use heuristics based on general characteristics of the data to evaluate the merit of feature subsets. A sub-category of filter methods that will be refer to as rankers, are methods that employ some criterion to score each feature and provide a ranking. From this ordering, several feature subsets can be chosen by manually setting There are three main approaches for feature selection: wrapper, filter and embedded. The wrapper approach (Kohavi, 1995; Kohavi and John,1996), uses an inducer as a black box along with a statistical re-sampling technique such as cross-validation to select the best feature subset according to some predictive measure. The embedded approach (see for instance Guyon and Elisseeff, 2003) is similar to the wrapper approach in the sense that the features are specifically selected for a certain inducer, but it selects the features in the process of learning.

Observation of Success Status of Employees in E-Learning Courses in Organizations with Data Mining

2017 ◽  
Vol 9 (1) ◽  
pp. 38-49
Author(s):  
Fatma Önay Koçoğlu ◽  
İlkim Ecem Emre ◽  
Çiğdem Selçukcan Erol
Keyword(s):  
Data Mining ◽  
Evaluation Criteria ◽  
Data Set ◽  
Completion Status ◽  
Completion Date ◽  
E Learning ◽  
Pharmaceutical Industries ◽  
Mining Methods ◽  
Using Data ◽  
Performance Results

The aim of this study is to analyze success in e-learning with data mining methods and find out potential patterns. In this context, 374.073 data of 2013-14 period taken from an institution serving in e-learning field in Turkey are used. Data set, which is collected from information technology, banking and pharmaceutical industries, includes success and industry of employees', trainings which they complete, whether the trainings are completed, first login and last logout dates, training completion date and duration of experience in training. Using this data set, success status of participants is observed by using data mining methods (C5.0, Random Forest and Gini). By observing using accuracy, error rate, specificity and f- score from performance evaluation criteria, C5.0 has chosen the algorithm which gives the best performance results. According to the results of the study, it has been determined that the sectors of the employees are not important, on the contrary the ones that are important are the completion status, the duration of experience and training.

Method for Processing Fluorescence Decay Kinetic Curves Using Data Mining Algorithms

2020 ◽  
Vol 87 (2) ◽  
pp. 333-344
Author(s):  
M. M. Yatskou ◽  
V. V. Skakun ◽  
V. V. Apanasovich
Keyword(s):  
Data Mining ◽  
Fluorescence Decay ◽  
Decay Kinetic ◽  
Kinetic Curves ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Mining Algorithms
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