What refers to various techniques used to find patterns in large data volumes?

The term that refers to various techniques used to find patterns in large data volumes is data mining tools. Data mining encompasses a range of statistical and computational techniques used to analyze large datasets and discover meaningful patterns, trends, and relationships within that data. It involves processes such as classification, regression, clustering, and association rule learning, which collectively enable organizations to extract insights from their data.

Data mining tools facilitate this process by providing functionalities for data collection, preparation, exploration, and modeling. They help analysts and data scientists to efficiently visualize data, automate the discovery of patterns, and ultimately derive actionable insights that can influence decision-making and strategy.

The other options focus on specific aspects or types of analysis rather than the broader suite of techniques involved in data mining. For example, outlier analysis is a specific technique used to identify abnormal items within data, while market basket analysis is a method used primarily in retail to determine the purchase behavior of consumers. A "cube" generally refers to a data structure used in online analytical processing (OLAP) for multi-dimensional data analysis and isn't directly associated with the broader techniques of data mining.