What is typically analyzed to determine data relationships in data mining?

The analysis of stored data is fundamental to determining data relationships in data mining. Stored data, often found in databases, includes historical records that can be examined for patterns, trends, and correlations. Data mining techniques can uncover insights from this data by leveraging various algorithms and statistical methods.

When you analyze stored data, you can apply processes such as clustering, classification, regression, and association rule learning to extract meaningful information that reflects relationships between different data points. This stored information serves as the foundation upon which analyses are built, allowing organizations to make informed decisions based on historical patterns.

In contrast, while generated reports may summarize findings, they do not offer the raw data necessary for in-depth relationship analysis. Live transaction data, although relevant for understanding current behaviors, is typically transient and may not provide the comprehensive historical context needed for extensive data mining. External market data can be valuable as well, but it primarily provides insights into broader trends rather than the specific internal relationships found within an organization's own data. Thus, the analysis of stored data stands out as the core element in data mining for establishing data relationships.