In real world datasets, lots of redundant and conflicting data exists. The performance of a classification algorithm in data mining is greatly affected by noisy information (i.e. redundant and conflicting data). These arameters not only increase the cost of mining process, but also degrade the detection performance of the classifiers. They have to be removed to increase the efficiency and accuracy of the classifiers. This process is called as the tuning of the dataset.Insight into Data pre-processing: Theory and Practice is the master reference that practitioners and researchers have long been seeking. It is also the obvious choice for academic and industry people.Features-1.Provides in-depth, practical coverage of essential data preprocessing topics, including data cleaning, integration, transformation, reduction and discretization. 2.Addresses the method to deals with redundant and conflicting pattern.

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