What is the goal of data cleaning in the data mining process?

The goal of data cleaning in the data mining process centers around the removal of inaccuracies and inconsistencies in the dataset. Data cleaning is a crucial step because it ensures that the data used for analysis is accurate, reliable, and relevant. Inaccurate data can lead to misleading insights and poor decision-making, while inconsistencies—such as different formats for the same type of data—can hinder data integration and analysis efforts. By addressing these issues, data cleaning helps to improve the overall quality of the data, leading to more valid and trustworthy outcomes from the data mining efforts.

In contrast, while transforming data into a usable format, visualizing data insights, and integrating various data sources are important aspects of the data mining process, they do not specifically address the primary goal of data cleaning. Transforming data is more about preparing it for analysis, visualization focuses on presenting insights, and integration deals with combining data from multiple sources. These activities may occur after data cleaning has taken place.