Which technique is NOT commonly used in Data Mining?

Batch processing is typically associated with the handling and manipulation of large sets of data for tasks such as data storage, retrieval, and maintenance, rather than for the discovery of patterns or predictive modeling, which is the focus of data mining techniques. Data mining involves extracting hidden insights from large datasets, and it often employs methods such as neural networks, fuzzy logic, and genetic algorithms for these purposes.

Neural networks are designed to recognize patterns through layers of nodes, making them ideal for tasks such as classification and regression. Fuzzy logic allows for reasoning and decision-making in situations with uncertainty, providing a means to handle imprecise data effectively. Genetic algorithms are optimization methods inspired by the process of natural selection, often used for searching through complex problem spaces to identify optimal solutions.

In contrast, batch processing is more about performing operations on data in bulk or at scheduled intervals, typically without the emphasis on uncovering insights or relationships that characterizes data mining processes. This fundamental difference in focus highlights why batch processing is not commonly recognized as a standard technique in data mining.