Vijayawada, AP, India
From November 2013
The majority of the Machine Learning and Data Mining applications can easily be applicable only on discrete features. 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.
• Data Preparation using dynamic connection to Phishtank web portal.
• Data conversion for Unrealized Data Sets
• Data preprocessing for noisy and numerical values Discritization.
• Applying Improved C45 algorithm for building decision rules (improving attribute selection measure.
• Applying filtering approach for optimal decision rules (K-anonymity).
Qualifications & Certifications
Board of Secondary Education
Board of Intermediate Education
University / Board
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