Usage of Classification based Association for Removal of Noisy Attributes
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10.5120/18968-5716 |
Nalini Yadav, K Rajeswari and V V Vaithiyanathan. Article: Usage of Classification based Association for Removal of Noisy Attributes. International Journal of Computer Applications 108(13):1-5, December 2014. Full text available. BibTeX
@article{key:article,
author = {Nalini Yadav and K. Rajeswari and V. V. Vaithiyanathan},
title = {Article: Usage of Classification based Association for Removal of Noisy Attributes},
journal = {International Journal of Computer Applications},
year = {2014},
volume = {108},
number = {13},
pages = {1-5},
month = {December},
note = {Full text available}
}
Abstract
Data mining is a process of extracting knowledge from underlying huge multidimensional data. Data mining techniques discovers hidden patterns from a given data. Classification is one of the techniques of data mining. Data Classification consists of categorization of data under the known class labels for its most effective and efficient utilization. There are different algorithms available for classification. Association rule mining is used to generate the rules for strongly associated attributes. It helps to uncover the association between seemingly unrelated attributes. Association rules are identified by analyzing patterns which satisfies the confidence and support criteria. This paper explains pre-processing for a given bank data set along with classification and association rule mining. Association followed by classification method helps in finding the noisy data attributes. Experimental setup uses WEKA tool for data mining. WEKA is a collection of machine learning algorithms for data mining tasks. Experiment has shown that classification with guidance of strongly supported rules from association rule mining helps in removal of noise and has increased the accuracy of classifier for a given data set.
References
- Han J, Kamber M, Pei J. Data Mining: Concepts and Techniques. 3rd ed. Burlington, Massachusetts, USA: Morgan Kaufmann Publishers, 2011.
- Shah C, Jivani A. Comparison of Data Mining Classification Algorithms for Breast Cancer Prediction. In: IEEE 2013 Fourth International Conference on Computing, Communications and Networking Technologies; 4-6 July 2013; Tiruchengode, India.
- Rajeswari K, Vaithiyannathan V. Mining Association Rules Using Hash Table. Int J Comput Appl 2012; 57: 7-11.
- Rajeswari K, Vaithiyannathan V. Heart Disease Diagnosis: An Efficient Decision Support System Based on Fuzzy Logic and Genetic Algorithm. Int J Decision Sci, Risk Manage 2011; 3; 81-97.
- Witten I, Frank E, Hall M. Data Mining: Practical Machine Learning Tools and Techniques. 3rd ed. Burlington, Massachusetts, USA: Morgan Kaufmann Publishers, 2011.
- Borkar S, Rajeswari K. Attributes Selection for Predicting Students' Academic Performance using Education Data Mining and Artificial Neural Network. Int J Comput Appl 2014; 86: 25-29
- Anwar A, Ahmed N. Knowledge Mining in Supervised and Unsupervised Assessment Data of Students' Performance. In: IPCSIT 2011 Second International Conference on Networking and Information Technology; Singapore