Data Preprocessing: The Techniques for Preparing Clean and Quality Data for Data Analytics Process

Citations Over TimeTop 10% of 2021 papers

Abstract

The model and pattern for real time data mining have an important role for decision making. The meaningful real time data mining is basically depends on the quality of data while row or rough data available at warehouse. The data available at warehouse can be in any format, it may huge or it may unstructured. These kinds of data require some process to enhance the efficiency of data analysis. The process to make it ready to use is called data preprocessing. There can be many activities for data preprocessing such as data transformation, data cleaning, data integration, data optimization and data conversion which are use to converting the rough data to quality data. The data preprocessing techniques are the vital step for the data mining. The analyzed result will be good as far as data quality is good. This paper is about the different data preprocessing techniques which can be use for preparing the quality data for the data analysis for the available rough data.

Related Papers

• → Data Preprocessing: The Techniques for Preparing Clean and Quality Data for Data Analytics Process(2021)53 cited
• Effectiveness of Data Preprocessing for Data Mining(2014)
• → POLYMORPHIC SBD PREPROCESSOR: A PREPROCESSING APPROACH FOR SOCIAL BIG DATA(2020)4 cited
• → Preprocessing Methods and Pipelines of Data Mining: An Overview(2019)13 cited
• → Minable Data Warehouse(2009)