Authors : Karuna C. Khobragade and Pankaj B. Dhumane
Page Nos : 106-117
Description :
The process of extracting relevant patterns and models from a large dataset is referred to as data mining. In a decision-making task, these models and patterns are quite useful. The quality of data is crucial for data mining. Missing values, noisy data, incomplete data, inconsistent data, and outlier data are all common characteristics of raw data. As a result, processing these data prior to mining is critical. Data pre-processing is a necessary step in improving data efficiency. Data pre-processing is a common data mining stage that involves the preparation and manipulation of a dataset while also attempting to improve the efficiency of knowledge discovery. Cleaning, integration, transformation, and reduction are some examples of pre-processing procedures. This research provides a comprehensive overview of data preparation strategies used in data mining.
