What does data mining aim to achieve in the context of predictive analytics?

Data mining is a crucial process in predictive analytics, primarily focused on discovering patterns and insights from data. This involves the application of various statistical and machine learning techniques to analyze vast amounts of data and identify trends, relationships, or anomalies that may not have been obvious initially. By extracting meaningful information, organizations can make informed predictions or decisions based on the underlying patterns observed in the data.

The other options do not capture the primary goal of data mining in predictive analytics. While creating new datasets, cleaning erroneous data, and storing large volumes of data are important tasks in data management and preparation, they do not specifically relate to the core objective of seeking out trends and insights. Data mining itself is about understanding the complexities of the data and leveraging that understanding for predictive purposes, making option C the most accurate representation of the aim of data mining in this context.