[PDF] Top 20 Parametric Comparisons of Classification Techniques in Data Mining Applications
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Parametric Comparisons of Classification Techniques in Data Mining Applications
... IJEDR1602194 International Journal of Engineering Development and Research (www.ijedr.org) 1121 implemented Decision Tree algorithms i.e. J48-graft and LAD tree and also Machine Learning algorithms i.e. Radial Basis ... See full document
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1. Educational data mining –applications and techniques
... performance data, the data mining classification techniques such as – Decision tree- Random Tree and J48 classification models were built with 10 cross validation fold using ... See full document
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1. Applications of data mining classification techniques on predicting breast cancer disease
... and the leaf node represent classes. It is a graphical representation of possible solutions based on condition from these solutions optimum course of action is carried out. In our work, we have used four decision tree ... See full document
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Data Mining: Techniques, Tools and Applications
... for data preprocessing, classification, clustering, association rules and ...relational data mining. Data file can be used in any format like ARFF (attribute relation file format), CSV ... See full document
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Data Mining: A Study on Various Applications and Techniques
... unknown data values by using the known values. Ex. Classification, Regression, Time series analysis, Prediction ...the data mining applications are aimed to predict the future state of ... See full document
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Survey Of Classification Techniques In Data Mining
... The choice of which specific learning algorithm we should use is a critical step. Once preliminary testing is judged to be satisfactory, the classifier (mapping from unlabeled instances to classes) is available for ... See full document
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DATA MINING TECHNIQUES IN E-COMMERCE APPLICATIONS
... as data clustering. Compared to data classification it is a challenging ...the data without the class labels and infers groups of elements just by merely examining their ...the ... See full document
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Applications of Data Mining Techniques In Health Insurance
... It is a non-parametric technique used for pattern recognition and Classification. Here, k closest training examples in the feature space are taken as input. Feature space is an abstract space defined by ... See full document
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Researchon Classification Techniques in Data Mining
... Data Mining is the process of analyzing data from different perspective and gaining the knowledge from large amount of large ...data[1]. Data mining is growing in various ... See full document
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Application of data mining techniques for medical data classification: a review
... practicality, data mining applications may benefit the healthcare industry ...the data is. To ensure the success of data mining applications, the capture, storage, ... See full document
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Conceptual Review of clustering techniques in...
... scale data mining and clustering techniques are likely to adapt more local and instance-driven ...that mining long patterns can be very ...of applications. Third, we conclude that ... See full document
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A Comparative Study of Classification Techniques in Data Mining Algorithms
... domains applications. SVMs are typically used for learning classification, regression or ranking ...based classification is not directly depend on the dimension of classified ...accurate ... See full document
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Detailed Analysis of Classification Techniques in Data Mining
... A Naïve Bayes classifier is a simple probabilistic classifier and works well for many applications, especially in text classification. Naïve bayes is a supervised and statistical learning method [19].This ... See full document
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Comparative Analysis of Various Data Mining Techniques on Educational Datasets
... Bayesian classification is based on Bayes ...the data tuple X belongs to a specified class ...observe data tuple ...the data tuple is confined to the University described by attribute ... See full document
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An investigation into the issues of multi-agent data mining
... of data sources scattered over a network and a group of DM agents that allow a user to data mine those data sources without needing to know the location of the supporting data, nor how the ... See full document
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PREDICT THE USAGE OF LAPTOPS AMONG STUDENTS IN RURAL AREAS USING WEKA TOOL
... In modern days, the usage of laptops is increased in the rural areas due to the government has issued the free laptops to the school and college students. The government’s intention on distribution of free laptops is to ... See full document
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DECISION TREE ANALYSIS ON J48 AND RANDOM FOREST ALGORITHM FOR DATA MINING USING BREAST CANCER MICROARRAY DATASET.
... clustering techniques we can further identify dense and sparse regions in object space and can discover overall distribution pattern and correlations among data ...attributes. Classification approach ... See full document
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APPLICATION OF DATA MINING IN HEALTH CARE
... Weka consist a variety of machine learning algorithms for various tasks of data mining. These algorithms are useful in two ways whether user can apply the algorithm directly on the dataset or user is free ... See full document
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TOWARDS MOBILE USERS SATISFACTION IN TELECOMMUNICATION NETWORKS USING DATA MINING CLASSIFIERS
... analyze data and complement this with Text mining, in order to have an holistic way of drawing inferences from both structured and unstructured data for the purpose of decision ... See full document
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A Model For Predicting Ischemic Stroke Using Data Mining Algorithms
... - Neural Network: In the early 20th century it was developed [13]. Before the introduction of decision trees and the Support Vector Machine (SVM) it was regarded as the best classification algorithm [14]. This was ... See full document
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