Review of Data Pre-processing Techniques for Classification
Keywords:
Data Mining, Noise, Preprocessing Dataset, SkewnessAbstract
Data mining is the process of extraction useful patterns from a huge dataset. These models and patterns have an effective role in a decision making task. Data mining basically depends on the quality of data. Raw data usually susceptible to missing values, noisy data, incomplete data, inconsistent data and outlier data. So it is important for these data to be pre-processed before being classified using classification algorithms. Data pre-processing is one of the most data mining steps which deals with data preparation and transformation. Preprocessed data make knowledge discovery more efficient. Preprocessing includes several techniques like cleaning, integration, transformation and data reduction. This paper shows the various preprocessing techniques applied to the classification.
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