What is the primary outcome of pattern evaluation in data mining?

The primary outcome of pattern evaluation in data mining is to assess the quality of discovered patterns. This step is crucial because it involves validating whether the patterns found in the data are significant, useful, and actionable. Evaluating patterns includes checking for metrics such as accuracy, reliability, and potential for generalization, meaning how well these patterns can be applied to new, unseen data.

This process helps ensure that the insights derived from the data mining process are not just coincidental or artifacts of noise in the data. By focusing on assessing the quality of the patterns, practitioners can filter out less valuable or even misleading patterns and concentrate on those that truly provide insights into the underlying data relationships. Thus, effective pattern evaluation is key to delivering actionable intelligence from the mined data.

In contrast, other choices focus on different aspects of data handling and processing rather than the evaluation of discovered patterns. Data visualization pertains to representing data visually, data cleaning involves preprocessing data to ensure quality prior to mining, and data integration refers to combining multiple datasets into a cohesive format. While these processes are important in the overall workflow of data mining, they do not specifically address the evaluation of the patterns themselves.