Which of the following is primarily used for knowledge discovery?

Data mining is primarily used for knowledge discovery because it involves the process of analyzing large sets of data to identify patterns, relationships, and insights that can inform decision-making. This field utilizes various techniques such as statistical analysis, machine learning, and pattern recognition to uncover hidden knowledge from raw data.

In knowledge discovery, the goal is to turn data into information and ultimately into knowledge, which can then be used for predictive analytics, trend analysis, and other forms of decision support. Data mining specifically focuses on extracting meaningful information from datasets, making it essential for understanding and efficiently using data in various applications.

The other options, while relevant to different facets of information technology, do not directly focus on knowledge discovery in the same manner. Expert systems are designed to mimic human decision-making in specific areas but rely on established knowledge rather than discovering new insights from data. Transaction processing systems are configured to manage and record daily transactions efficiently rather than to uncover new knowledge from data sets. Case-based reasoning utilizes previous cases to solve new problems but does not inherently include the processes used in data mining to extract and discover knowledge from vast data repositories.