Refining the Noisy Candidates in Learning Data for Improving Classification Performance
Journal Title: International journal of Emerging Trends in Science and Technology - Year 2015, Vol 2, Issue 10
Abstract
The data mining is a technique are used to learn a pattern and identify the pattern among a huge amount of data using computer based algorithms or programs. In this technique the two main technique of learning is found first supervised and second the unsupervised. But the quality of learning is depends upon the amount of data and quality of learning pattern. Therefore, in order to improve the quality of learning data the pre-processing is performed on data. In this paper a review on the pre-processing techniques and the data quality enhancement techniques is reported. Further the key issues and challenges are addressed for improving the learning data quality. Finally for resolving the issues a new technique is proposed in this paper and their future extension of the work is also provided
Authors and Affiliations
Maya Yadav
Keywords
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