Application of Data Mining Algorithms in Determination of Voting Tendencies in Turkey

2021 ◽  
pp. 134-149
Author(s):  
Ali Bayır ◽  
Sevinç Gülseçen ◽  
Gökhan Türkmen
Keyword(s):  
Data Mining ◽  
Current Data ◽  
Data Set ◽  
Data Mining Techniques ◽  
Human Behaviors ◽  
Political Sciences ◽  
Data Mining Algorithms ◽  
Number Of Factors ◽  
Mining Algorithms

Political elections are influenced by a number of factors such as political tendencies, voters' perceptions, and preferences. The results of a political election could also be based on specific attributes of candidates: age, gender, occupancy, education, etc. Although it is very difficult to understand all the factors which could have influenced the outcome of the election, many of the attributes mentioned above could be included in a data set, and by using current data mining techniques, undiscovered patterns can be revealed. Despite unpredictability of human behaviors and/or choices involved, data mining techniques still could help in predicting the election outcomes. In this study, the results of the survey prepared by KONDA Research and Consultancy Company before 2011 elections in Turkey were used as raw data. This study may help in understanding how data mining methods and techniques could be used in political sciences research. The study may also reveal whether voting tendencies in elections could be a factor for the outcome of the election.

Review of Data Mining Techniques Used in Healthcare

2021 ◽  
pp. 1-26
Author(s):  
Usha Gupta ◽  
Kamlesh Sharma
Keyword(s):  
Data Mining ◽  
Vital Role ◽  
Mining Machine ◽  
Support Vector ◽  
Data Set ◽  
Data Mining Techniques ◽  
Network Support ◽  
Data Mining Algorithms ◽  
Clinical Databases ◽  
Mining Algorithms

Data mining plays a vital role in converting the medical data like text, image, and graphs into meaningful new data, which helps to take the better decision. In this chapter, an overview of the current research is discussed using the data mining techniques for the finding, analysis, and prediction of various diseases. The focus of this study is to identify the well-performing data mining algorithms used on medical and clinical databases. Multiple algorithms have been identified: text-based mining, association rule-based mining, pattern-based mining, keyword-based mining, machine learning, neural network support vector machine, apriori algorithm, k-means clustering, and natural language. Analyses of the algorithm show that there is no single algorithm or model more suitable for diagnosing or predicting diseases. In some scenarios, some algorithms work very well but not in another data set. There are many examples in clinical or medical research where the combination of different algorithms gives good results.

scholarly journals Analisis Algoritma Apriori untuk Mendukung Strategi Promosi Perguruan Tinggi

2019 ◽  
Vol 1 (1) ◽  
pp. 49
Author(s):  
Haryo Kusumo ◽  
Eko Sediyono ◽  
Marwata Marwata
Keyword(s):  
Data Mining ◽  
A Priori ◽  
Promotion Strategy ◽  
Data Mining Techniques ◽  
Data Mining Algorithms ◽  
New Students ◽  
The Right ◽  
The Cost ◽  
Mining Algorithms

<p><em>Every company and organization that wants to survive needs to determine the effectiveness of the right promotion strategy. Determination of the right promotion strategy will be able to reduce the cost of promotion and achieve the right promotional goals. One way that can be done to determine the promotion strategy is to use data mining techniques. Data mining techniques used in this case are using the Apriori algorithm. A priori algorithm is one of the classic data mining algorithms. A priori algorithms are used so that computers can learn the rules of association, look for patterns of relationships between one or more items in a dataset. This study is conducted by observing several research variables that are often considered by universities in determining their promotion goals, namely school, region, and department. The results of this study are in the form of interesting patterns resulting from data mining which is important information to support the right promotion strategy in getting new students.</em></p>

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.

Incremental Discovery of Fuzzy Functional Dependencies

2011 ◽  
pp. 615-633 ◽  
Author(s):  
Shyue-Liang Wang ◽  
Ju-Wen Shen ◽  
Tuzng-Pei Hong
Keyword(s):  
Data Mining ◽  
Relational Databases ◽  
Search Algorithm ◽  
Current Data ◽  
Research Interest ◽  
Functional Dependencies ◽  
Incremental Search ◽  
Data Mining Techniques ◽  
Analysis Technique ◽  
Mining Algorithms

Mining functional dependencies (FDs) from databases has been identified as an important database analysis technique. It has received considerable research interest in recent years. However, most current data mining techniques for determining functional dependencies deal only with crisp databases. Although various forms of fuzzy functional dependencies (FFDs) have been proposed for fuzzy databases, they emphasized conceptual viewpoints and only a few mining algorithms are given. In this research, we propose methods to validate and incrementally search for FFDs from similarity-based fuzzy relational databases. For a given pair of attributes, the validation of FFDs is based on fuzzy projection and fuzzy selection operations. In addition, the property that FFDs are monotonic in the sense that r1 ? r2 implies FDa(r1) ? FDa(r2) is shown. An incremental search algorithm for FFDs based on this property is then presented. Experimental results showing the behavior of the search algorithm are discussed.

The Development of Data Mining System Based on Cloud Computing

2014 ◽  
Vol 926-930 ◽  
pp. 2280-2283
Author(s):  
Qiong Ren
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Current Data ◽  
Mining System ◽  
Computing Platform ◽  
Data Mining Algorithms ◽  
Cloud Computing Platform ◽  
Data Mining System ◽  
Process Cost ◽  
Mining Algorithms

With the increasing of input data size, process cost will be very long, for the explosive growth of the Internet data even reached the point of single machine can handle. This article mainly introduces the architecture of the concept of cloud computing and, the mainstream of the analysis of the current data mining algorithms, based on cloud computing to develop the data mining system, providing the operation feasibility of data mining in cloud computing platform, having strong guiding significance.

scholarly journals A Comparative Evaluation of Meta Classification Algorithms with Smokers Lung Data

2018 ◽  
Vol 7 (4.36) ◽  
pp. 845 ◽  
Author(s):  
K. Kavitha ◽  
K. Rohini ◽  
G. Suseendran
Keyword(s):  
Data Mining ◽  
Web Mining ◽  
Lung Function Test ◽  
Performance Comparison ◽  
Business Organizations ◽  
Science Data ◽  
Data Set ◽  
Data Mining Algorithms ◽  
Report Data ◽  
Mining Algorithms

Data mining is the course of process during which knowledge is extracted through interesting patterns recognized from large amount of data. It is one of the knowledge exploring areas which is widely used in the field of computer science. Data mining is an inter-disciplinary area which has great impact on various other fields such as data analytics in business organizations, medical forecasting and diagnosis, market analysis, statistical analysis and forecasting, predictive analysis in various other fields. Data mining has multiple forms such as text mining, web mining, visual mining, spatial mining, knowledge mining and distributed mining. In general the process of data mining has many tasks from pre-processing. The actual task of data mining starts after the preprocessing task. This work deals with the analysis and comparison of the various Data mining algorithms particularly Meta classifiers based upon performance and accuracy. This work is under medical domain, which is using the lung function test report data along with the smoking data. This medical data set has been created from the raw data obtained from the hospital. In this paper work, we have analyzed the performance of Meta classifiers for classifying the files. Initially the performances of Meta and Rule classifiers are analyzed observed and found that the Meta classifier is more efficient than the Rule classifiers in Weka tool. The implementation work then continued with the performance comparison between the different types of classification algorithm among which the Meta classifiers showed comparatively higher accuracy in the process of classification. The four Meta classifier algorithms which are widely explored using the Weka tool namely Bagging, Attribute Selected Classifier, Logit Boost and Classification via Regression are used to classify this medical dataset and the result so obtained has been evaluated and compared to recognize the best among the classifier.  

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.

Body Weight Prediction of Thalli Sheep Reared in Southern Punjab Using Different Data Mining Algorithms

2021 ◽  
Vol 58 (2) ◽  
pp. 29-38
Author(s):  
Ansar Abbas ◽  
Muhammad Aman Ullah ◽  
Abdul Waheed
Keyword(s):  
Data Mining ◽  
Body Weight ◽  
Goodness Of Fit ◽  
The Body ◽  
Classification And Regression Tree ◽  
Body Measurements ◽  
Data Set ◽  
Data Mining Algorithms ◽  
Exhaustive Chaid ◽  
Mining Algorithms

This study is conducted to predict the body weight (BW) for Thalli sheep of southern Punjab from different body measurements. In the BW prediction, several body measurements viz., withers height, body length, head length, head width, ear length, ear width, neck length, neck width, heart girth, rump length, rump width, tail length, barrel depth and sacral pelvic width are used as predictors. The data mining algorithms such as Chi-square Automatic Interaction Detector (CHAID), Exhaustive CHAID, Classification and Regression Tree (CART) and Artificial Neural Network (ANN) are used to predict the BW for a total of 85 female Thalli sheep. The data set is partitioned into training (80 %) and test (20 %) sets before the algorithms are used. The minimum number of parent (4) and child nodes (2) are set in order to ensure their predictive ability. The R2 % and RMSE values for CHAID, Exhaustive CHAID, ANN and CART algorithms are 67.38(1.003), 64.37(1.049), 61.45(1.093) and 59.02(1.125), respectively. The mostsignificant predictor is BL in the BW prediction of Thalli sheep. The heaviest BW average of 9.596 kg is obtained from the subgroup of those having BL > 25.000 inches. On behalf of the several goodness of fit criteria, we conclude that the CHAID algorithm performance is better in order to predict the BW of Thalli sheep and more suitable decision tree diagram visually. Also, the obtained CHAID results may help to determine body measurements positively associated with BW for developing better selection strategies with the scope of indirect selection criteria.

Identifying Decision Structures Underlying Activity Patterns: An Exploration of Data Mining Algorithms

2000 ◽  
Vol 1718 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Geert Wets ◽  
Koen Vanhoof ◽  
Theo Arentze ◽  
Harry Timmermans
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Logit Model ◽  
Goodness Of Fit ◽  
Travel Demand ◽  
Activity Patterns ◽  
Future Research ◽  
Data Set ◽  
Data Mining Algorithms ◽  
Mining Algorithms

The utility-maximizing framework—in particular, the logit model—is the dominantly used framework in transportation demand modeling. Computational process modeling has been introduced as an alternative approach to deal with the complexity of activity-based models of travel demand. Current rule-based systems, however, lack a methodology to derive rules from data. The relevance and performance of data-mining algorithms that potentially can provide the required methodology are explored. In particular, the C4 algorithm is applied to derive a decision tree for transport mode choice in the context of activity scheduling from a large activity diary data set. The algorithm is compared with both an alternative method of inducing decision trees (CHAID) and a logit model on the basis of goodness-of-fit on the same data set. The ratio of correctly predicted cases of a holdout sample is almost identical for the three methods. This suggests that for data sets of comparable complexity, the accuracy of predictions does not provide grounds for either rejecting or choosing the C4 method. However, the method may have advantages related to robustness. Future research is required to determine the ability of decision tree-based models in predicting behavioral change.

scholarly journals Analysis And Detection of Diabetes Using Data Mining Techniques – Efficiency Comparison

2021 ◽  
pp. 73-79
Author(s):  
G. Ramadevi ◽  
Srujitha Yeruva ◽  
P. Sravanthi ◽  
P. Eknath Vamsi ◽  
S. Jaya Prakash
Keyword(s):  
Data Mining ◽  
Clustering Algorithms ◽  
Medical Data ◽  
Healthcare Sector ◽  
Data Mining Techniques ◽  
Data Mining Algorithms ◽  
Use Of Data ◽  
Efficiency Comparison ◽  
Using Data ◽  
Mining Algorithms

In a digitized world, data is growing exponentially and it is difficult to analyze the data and give the results. Data mining techniques play an important role in healthcare sector - BigData. By making use of Data mining algorithms it is possible to analyze, detect and predict the presence of disease which helps doctors to detect the disease early and in decision making. The objective of data mining techniques used is to design an automated tool that notifies the patient’s treatment history disease and medical data to doctors. Data mining techniques are very much useful in analyzing medical data to achieve meaningful and practical patterns. This project works on diabetes medical data, classification and clustering algorithms like (OPTICS, NAIVEBAYES, and BRICH) are implemented and the efficiency of the same is examined.

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