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Data preprocessing describes any type of processing performed on raw data to prepare it for another processing procedure. Commonly used as a preliminary data mining practice, data preprocessing transforms the data into a format that will be more easily and effectively processed for the purpose of the user -- for example, in a neural network.
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Scope and Importance :
Real world data are generally Incomplete: lacking attribute values, lacking certain attributes of interest, or containing only aggregate data Noisy: containing errors or outliers Inconsistent: containing discrepancies in codes or names Tasks in data pre processing Data cleaning: fill in missing values, smooth noisy data, identify or remove outliers, and resolve inconsistencies. Data integration: using multiple databases, data cubes, or files. Data transformation: normalization and aggregation. Data reduction: reducing the volume but producing the same or similar analytical results. Data discretization: part of data reduction, replacing numerical attributes with nominal ones.
Data pre -processing is a data mining technique that involves transforming raw data into an understandable format. Real-world data is often incomplete, inconsistent, and/or lacking in certain behaviors or trends, and is likely to contain many errors. Data preprocessing is a proven method of resolving such issues. Data preprocessing prepares raw data for further processing.
The Data Pre-processing market has been achieving strong revenues. The Global Market Statistics shows that it has reached a revenue height of 9.00USD (billion), 4.15 USD (billion) and 5.12 USD (billion) for services, hardware and software, respectively for the year 2013. The global market statistic also shows a revenue forecast for the global big data market from 2011 to 2017. For 2016, the source projects the global big data market is predicted to grow to more than 45 billion U.S. dollars in revenue.
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This page was last updated on June 2, 2020