Abstract
With recent advances in computer technology, large amounts of data could be collected and stored. But all this data becomes more useful when it is analyzed and some dependencies and correlations are detected. This can be accomplished with machine learning algorithms. WEKA (Waikato environment for knowledge analysis) is a collection of machine learning algorithms implemented in Java. WEKA consists of a large number of learning schemes for classification and regression numeric prediction. So, by using this we can find out the prediction value of dataset and the data which we stored can be seen in different forms in the form of matrix, graph, curve, tree, etc. In this paper, we are researching or comparing the results of the three classifiers, the classifiers we are using such as J48, Naïve Bayes, and preprocess the data. We compare the results which provide easy way to understand all the datasets and its condition.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Eleonora Brtka, Vladimir Brtka, Visnja Ognjenovic and Ivana Berkovic, “The data visualization technique in e-learning system”, IEEE 10th Jubilee International Symposium on Intelligent Systems and Informatics (September 20–22, 2012).
P. Nevlud, M. Bures, L. Kapicak and J. Zdralek, Anomaly based Network Intrusion Detection Methods Advances in Electrical and Electronic Engineering, pp. 468–474, (2013).
M. Mayilvaganan, D. Kalpanadevi “Comparison of Classification Techniques for predicting the performance Of Students Academic Environment”, 2014 India, coimbatore International Conference on Communication and Network Technologies (ICCNT).
S. Ummugulthum Natchiar, Dr. S. Baulkani, “Customer Relationship Management Classification using Data Mining Techniques” International Conference on Science, Engineering and Management Research (ICSEMR 2014).
Sabri Serkan Güllüoğlu, “Segmenting Customers With Data Mining Techniques”, ISBN: 978-1-4799-6376-8/15/©(2015) IEEE.
Patricia Morreale, Steve Holtz, Allan Goncalves, “Data Mining and Analysis of Large Scale Time Series Network Data”, 2013 27th International Conference on Advanced Information Networking and Applications Workshops.
Darshana Parikh, Priyanka Tirkha, “Data Mining & Data Stream Mining—Open Source Tools” International Journal of Innovative Research in Science, Engineering and Technology, Vol. 2, Issue (10, October 2013).
Charles A. Fowler and Robert J. Hammell Converting PCAPs into Weka Mineable Data copyright 2014 IEEE SNPD 2014, (June 30–July 2, 2014), Las Vegas, USA.
Swasti Singhal, Monika Jena, A Study on WEKA Tool for Data Preprocessing, Classification and Clustering, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-2, Issue-6, (May 2014).
C. M. Velu, K. R. Kashwan, “Visual Data Mining Techniques for Classification of Diabetic Patients”, Maharashtra, INDIA, 2013 3rd IEEE International Advance Computing Conference (IACC).
D. Rajeswara rao, Vidyullata Pellakuri, SathishTallam, T. Ramya “Harika Performance Analysis of Classification Algorithms using healthcare dataset” (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (2), 2015.
Manisha Girotra, Kanika Nagpal, Saloni Minocha, Neha Sharma, “Comparative Survey on Association Rule Mining Algorithms” International Journal of Computer Applications (0975–8887) Volume 84–No (10, December 2013).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this paper
Cite this paper
Dheeraj Pal, Alok Jain, Aradhana Saxena, Vaibhav Agarwal (2016). Comparing Various Classifier Techniques for Efficient Mining of Data. In: Satapathy, S., Bhatt, Y., Joshi, A., Mishra, D. (eds) Proceedings of the International Congress on Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 439. Springer, Singapore. https://doi.org/10.1007/978-981-10-0755-2_21
Download citation
DOI: https://doi.org/10.1007/978-981-10-0755-2_21
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-0754-5
Online ISBN: 978-981-10-0755-2
eBook Packages: EngineeringEngineering (R0)