An Empirical Evaluation of Data Mining Classification Algorithms

Data Mining is the process of extracting interesting knowledge from large datasets by joining methods from statistics and artificial intelligence with database management. Classification is one of the main functionality in the field of data mining. Classification is the forms of data analysis that c...

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Bibliographic Details
Published in:International journal of computer science and information security 2016-05, Vol.14 (5), p.142-142
Main Authors: Bhavsar, Hetal, Ganatra, Amit
Format: Article
Language:English
Subjects:
Algorithms
Classification
Classifiers
Data mining
Datasets
Decision trees
Mathematical models
Methods
Neural networks
Support vector machines
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Summary:Data Mining is the process of extracting interesting knowledge from large datasets by joining methods from statistics and artificial intelligence with database management. Classification is one of the main functionality in the field of data mining. Classification is the forms of data analysis that can be used to extract models describing important data classes The well known classification methods are Decision tree classification, Neuaral network classification, Naïve Bayes Classification, k-nearest neighbor classification and Support Vector Machine (SVM) classification. In this paper, we present the comparison of five classification algorithms, J48; which is based on C4.5 decision tree based learning, Multilayer perceptron (MLP); uses the multilayer feed forward neural network approach, Instance based K-nearest neighbour (IBK), Naive Bayse (NB), and Sequential Minimal Optimization (SMO); is an extension of support vector machine. Performance of these classification algorithms are compared with respect to classifier accuracy, error rates, building time of classifier and other statistical measures on WEKA tool. The result showed that there is no universal classification algorithm which works better for all the dataset.
ISSN:1947-5500