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.  

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.

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 Workload Optimization by Horizontal Aggregation in SQL for Data Mining Analysis

2021 ◽  
pp. 304-309
Author(s):  
Prasanna M. Rathod ◽  
Prof. Dr. Anjali B. Raut
Keyword(s):  
Data Mining ◽  
Relational Algebra ◽  
Data Migration ◽  
Data Sets ◽  
Data Set ◽  
Application Performance ◽  
Data Mining Algorithms ◽  
Mining Project ◽  
Pivot Methods ◽  
Mining Algorithms

Preparing a data set for analysis is generally the most time consuming task in a data mining project, requiring many complex SQL queries, joining tables, and aggregating columns. Existing SQL aggregations have limitations to prepare data sets because they return one column per aggregated group. In general, a significant manual effort is required to build data sets, where a horizontal layout is required. We propose simple, yet powerful, methods to generate SQL code to return aggregated columns in a horizontal tabular layout, returning a set of numbers instead of one number per row. This new class of functions is called horizontal aggregations. Horizontal aggregations build data sets with a horizontal denormalized layout (e.g., point-dimension, observation variable, instance-feature), which is the standard layout required by most data mining algorithms. We propose three fundamental methods to evaluate horizontal aggregations: ? CASE: Exploiting the programming CASE construct; ? SPJ: Based on standard relational algebra operators (SPJ queries); ? PIVOT: Using the PIVOT operator, which is offered by some DBMSs. Experiments with large tables compare the proposed query evaluation methods. Our CASE method has similar speed to the PIVOT operator and it is much faster than the SPJ method. In general, the CASE and PIVOT methods exhibit linear scalability, whereas the SPJ method does not. For query optimization the distance computation and nearest cluster in the k-means are based on SQL. Workload balancing is the assignment of work to processors in a way that maximizes application performance. The process of load balancing can be generalized into four basic steps: 1. Monitoring processor load and state; 2. Exchanging workload and state information between processors; 3. Decision making; 4. Data migration. The decision phase is triggered when the load imbalance is detected to calculate optimal data redistribution. In the fourth and last phase, data migrates from overloaded processors to under-loaded ones.

A Prediction System for Diagnosis of Diabetes Mellitus

2020 ◽  
Vol 17 (1) ◽  
pp. 6-9
Author(s):  
Ramya G. Franklin ◽  
B. Muthukumar
Keyword(s):  
Data Mining ◽  
Large Data ◽  
Heterogeneous Data ◽  
Common Disease ◽  
Prediction System ◽  
Health Issues ◽  
Data Set ◽  
Data Mining Algorithms ◽  
Growth Of Science ◽  
Mining Algorithms

The growth of Science is a priceless asset to the human and society. The plethora of high-end machines has made life a sophistication which in turn is paid back as health issues. The health care data are complex and large. This heterogeneous data are used to diagnose patient’s diseases. It is better to predict the diseases at an earlier stage that can save the life and also have an upper hand in controlling the diseases. Data mining approaches are very useful in analyzing the complex, heterogeneous and large data set. The mining algorithms extract the essential data set from the raw data. This paper presents a survey on the various data mining algorithms used in predicting a very common disease in day a today life “Diabetics Mellitus.” Over 246 million people in the world are diabetic with a majority of them being women. The WHO reports that by 2025 this number is expected to rise to over 380 million.

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.

scholarly journals Comparative Analysis of Kohonen-SOM and K-Means data mining algorithms based on Academic Activities

2013 ◽  
Vol 6 (1) ◽  
pp. 237-241
Author(s):  
Shaina Dhingra ◽  
Rimple Gilhotra ◽  
Ravishanker Ravishanker
Keyword(s):  
Data Mining ◽  
Comparative Analysis ◽  
Large Scale ◽  
Decision Makers ◽  
Data Set ◽  
Data Mining Algorithms ◽  
Academic Activities ◽  
Increasing Demand ◽  
Kohonen Som ◽  
Mining Algorithms

With the increasing demand of IT and subsequent growth in this sector, the high- dimensional data came into existence. Data Mining plays an important role in analyzing and extracting the useful information. The key information which is extracted from a huge pool of data is useful for decision makers. Clustering, one of the techniques of data mining is the mostly used methods of analyzing the data. In this paper, the approach of Kohonen SOM and K-Means and HAC are discussed. After that these three methods are used for analyzing the academic data set of the faculty members of particular university. Finally a comparative analysis of these algorithms are done against some parameters like number of clusters, error rate and accessing rate, etc.  This work will present new and improved results from large-scale datasets.

scholarly journals A comparative study of Distributed Large Scale Data Mining Algorithms

2020 ◽  
Author(s):  
Isha Sood ◽  
Varsha Sharma
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Large Data ◽  
Data Sets ◽  
Data Set ◽  
Data Mining Algorithms ◽  
Large Scale Data ◽  
Mapreduce Model ◽  
Mining Algorithms ◽  
Scale Data

Essentially, data mining concerns the computation of data and the identification of patterns and trends in the information so that we might decide or judge. Data mining concepts have been in use for years, but with the emergence of big data, they are even more common. In particular, the scalable mining of such large data sets is a difficult issue that has attached several recent findings. A few of these recent works use the MapReduce methodology to construct data mining models across the data set. In this article, we examine current approaches to large-scale data mining and compare their output to the MapReduce model. Based on our research, a system for data mining that combines MapReduce and sampling is implemented and addressed

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