Abstract
Data mining has gained immense popularity in various fields of medical, education and industry as well. Data mining is a process of predicting the result and extraction of useful information from huge dataset. In this paper, we have surveyed various data mining techniques. Further, performance of various data mining techniques, namely decision tree, random forest, naive Bayes, AdaBoost, multilayer perception neural network, radial basis function, sequential minimal optimization and decision stump, have been evaluated using UCI communities and crime dataset for classifying crime in US states. On the basis of results obtained, we found that the decision tree outperforms with 96.4% accuracy and minimal false-positive rate.
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Mani, Bharti Suri, Manoj Kumar (2018). Performance Evaluation of Data Mining Techniques. In: Mishra, D., Nayak, M., Joshi, A. (eds) Information and Communication Technology for Sustainable Development. Lecture Notes in Networks and Systems, vol 9. Springer, Singapore. https://doi.org/10.1007/978-981-10-3932-4_39
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DOI: https://doi.org/10.1007/978-981-10-3932-4_39
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