Journal of Analysis and Computation (JAC) (An International Peer Reviewed Journal), www.ijaconline.com, ISSN 0973-2861 Volume XIII, Issue I, January 2019 MRS. G. AMALA 1 ORANGE TOOL APPROACH FOR COMPARATIVE ANALYSIS OF SUPERVISED LEARNING ALGORITHM IN CLASSIFICATION MINING MRS. G. AMALA Assistant Professor of Computer Science, Nadar Saraswathi College of Arts and Science, Theni. ABSTRACT Data Mining is the technic to extract the hidden predictive data from large database. Data mining is a powerful new technology with great potential to help for all the fields focus on the most vital information in their data warehouse. Data mining is the automated prediction of trends and behaviors and it is of high speed which makes it easy for the users to analyze huge amount of data in less time. Data mining techniques classification is the most commonly used data mining technique which contains a set of pre classified samples to create a model which can classify the large set of data. This technique helps in deriving important information about data and metadata (data about data). The classification technics are applied by the learning algorithms such as Decision tree (DT), Support Vector Machines (SVM), Naive Bayes (NB) and Neural Network (NN) and these methods can handle both numerical and categorical attributes. This study will be implementing in Orange Tool and it will be applied in Iris Dataset. This study described the performance analysis of classification algorithm based on the correct and incorrect instances of data classification. The comparison will be taking the following parameters such as Precision, Recall, F- Measure, Accuracy and Root mean squared error. Keywords: Random Forest Algorithem, Data mining, Classification, Decision tree, Orange Tool, Precision, Recall. 1. INTRODUCTION Data Mining is defined as mining data from huge sets of data. In other words, we can say that data mining is the procedure of mining knowledge from data. The information or knowledge extracted so can be used for any of the following applications − Market Analysis Fraud Detection Customer Retention Production Control
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Journal of Analysis and Computation (JAC) (An International Peer Reviewed Journal), www.ijaconline.com, ISSN 0973-2861
Volume XIII, Issue I, January 2019
MRS. G. AMALA 1
ORANGE TOOL APPROACH FOR COMPARATIVE ANALYSIS OF
SUPERVISED LEARNING ALGORITHM IN CLASSIFICATION MINING
MRS. G. AMALA
Assistant Professor of Computer Science,
Nadar Saraswathi College of Arts and Science, Theni.
ABSTRACT
Data Mining is the technic to extract the hidden predictive data from large database. Data mining is a
powerful new technology with great potential to help for all the fields focus on the most vital information
in their data warehouse. Data mining is the automated prediction of trends and behaviors and it is of high
speed which makes it easy for the users to analyze huge amount of data in less time. Data mining
techniques classification is the most commonly used data mining technique which contains a set of pre
classified samples to create a model which can classify the large set of data. This technique helps in
deriving important information about data and metadata (data about data). The classification technics are
applied by the learning algorithms such as Decision tree (DT), Support Vector Machines (SVM), Naive
Bayes (NB) and Neural Network (NN) and these methods can handle both numerical and categorical
attributes. This study will be implementing in Orange Tool and it will be applied in Iris Dataset. This study
described the performance analysis of classification algorithm based on the correct and incorrect instances
of data classification. The comparison will be taking the following parameters such as Precision, Recall, F-
Measure, Accuracy and Root mean squared error.
Keywords: Random Forest Algorithem, Data mining, Classification, Decision tree, Orange Tool,
Precision, Recall.
1. INTRODUCTION
Data Mining is defined as mining data from huge sets of data. In other words, we can say
that data mining is the procedure of mining knowledge from data. The information or knowledge
extracted so can be used for any of the following applications −