Improved Discretization Based Decision Tree for Continuous Attributes
Journal Title: INTERNATIONAL JOURNAL OF COMPUTER TRENDS & TECHNOLOGY - Year 2013, Vol 5, Issue 5
Abstract
The majority of the Machine Learning and Data Mining applications can easily be applicable only on discrete features. However, data in solid world are sometimes continuous by nature. Even for algorithms that will directly encounter continuous features, learning is most often ineffective and effective. Hence discretization addresses this problem by finding the intervals of numbers which happen to be more concise to represent and specify. Discretization of continuous attributes is one of the important data preprocessing steps of knowledge extraction. The proposed improved discretization approach significantly reduces the IO cost and also requires one time sorting for numerical attributes which leads to a better performance in time dimension on rule mining algorithms. According to the experimental results, our algorithm acquires less execution time over the Entropy based algorithm and also adoptable for any attribute selection method by which the accuracy of rule mining is improved.
Authors and Affiliations
S. Jyothsna , G. Bharthi
Keywords
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