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MCAIM: Modified CAIM Discretization Algorithm for Classification

Shivani V. Vora, R. G. Mehta Published in Data Mining

International Journal of Applied Information Systems
Year of Publication 2012
© 2010 by IJAIS Journal
10.5120/ijais12-450542
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  1. Shivani V Vora and R G Mehta. Article: MCAIM: Modified CAIM Discretization Algorithm for Classification. International Journal of Applied Information Systems 3(5):42-50, July 2012. BibTeX

    @article{key:article,
    	author = "Shivani V. Vora and R. G. Mehta",
    	title = "Article: MCAIM: Modified CAIM Discretization Algorithm for Classification",
    	journal = "International Journal of Applied Information Systems",
    	year = 2012,
    	volume = 3,
    	number = 5,
    	pages = "42-50",
    	month = "July",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

Discretization is a process of dividing a continuous attribute into a finite set of intervals to generate an attribute with small number of distinct values, by associating discrete numerical value with each of the generated intervals. Discretization is usually performed prior to the learning process and has played an important role in data mining and knowledge discovery. The results of CAIM are not satisfactory in some cases, led us to modify the algorithm. The Modified CAIM (MCAIM) results are compared with other discretization techniques for classification accuracy and generated the outperforming results. The intervals generated by MCAIM discretization are more in numbers, so to reduce them, the CAIR criterion is used to merge the intervals in MCAIM discretization. It gives better classification accuracy and the reduced number of intervals.

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Keywords

Discretization, Class-attribute interdependency maximization, CAIM, MCAIM, CAIR