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DOI: 10.14569/IJACSA.2011.020306
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Knowledge discovery from database using an integration of clustering and classification

Author 1: Varun Kumar
Author 2: Nisha Rathee

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 2 Issue 3, 2011.

  • Abstract and Keywords
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Abstract: Clustering and classification are two important techniques of data mining. Classification is a supervised learning problem of assigning an object to one of several pre-defined categories based upon the attributes of the object. While, clustering is an unsupervised learning problem that group objects based upon distance or similarity. Each group is known as a cluster. In this paper we make use of a large database ‘Fisher’s Iris Dataset’ containing 5 attributes and 150 instances to perform an integration of clustering and classification techniques of data mining. We compared results of simple classification technique (using J48 classifier) with the results of integration of clustering and classification technique, based upon various parameters using WEKA (Waikato Environment for Knowledge Analysis), a Data Mining tool. The results of the experiment show that integration of clustering and classification gives promising results with utmost accuracy rate and robustness even when the data set is containing missing values.

Keywords: Data Mining; J48; KMEANS; WEKA; Fisher’s Iris dataset.

Varun Kumar and Nisha Rathee, “Knowledge discovery from database using an integration of clustering and classification” International Journal of Advanced Computer Science and Applications(IJACSA), 2(3), 2011. http://dx.doi.org/10.14569/IJACSA.2011.020306

@article{Kumar2011,
title = {Knowledge discovery from database using an integration of clustering and classification},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2011.020306},
url = {http://dx.doi.org/10.14569/IJACSA.2011.020306},
year = {2011},
publisher = {The Science and Information Organization},
volume = {2},
number = {3},
author = {Varun Kumar and Nisha Rathee}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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