Data cleaning and transformation techniques using cluster analysis in virtually integrated environment

Abstract

newline The fast growth of data science has offered significant potential newlineto build data-driven solutions. The basis for successful data analysis is newlinedata preprocessing. Preprocessing of data refers to a collection of newlinestrategies for improving raw data quality, such as elimination of newlineexternalization and imputation of missing information. Data newlinepreprocessing is a phase in the process of data mining and analytics newlinethat takes raw data and converts it into a format that computer and newlinemachine learning can understand and evaluate. Real-world data are newlinesometimes partial, incoherent, or missing from specific behaviors or newlinepatterns and may contain numerous mistakes. In addition, the newlinereal-world data are in different forms such as text, images, audio, video newlineetc., It may not just contain mistakes and inconsistencies but is often newlineincomplete and not regularly designed with uniformity. Data newlinepreprocessing is an established way to solve these problems. newlineThis research work proposed an integrated the data from newlinevarious sources such as distributed system, client-server systems, cloud newlinesystems, remote systems etc.