Which data mining techniques are effective for discovering patterns in large datasets used in AI?

The selection of K-means clustering and Principal Component Analysis (PCA) as effective data mining techniques for discovering patterns in large datasets used in AI is well-founded. K-means clustering is a popular algorithm used to partition a dataset into distinct groups or clusters based on the features of the data. Each cluster is represented by its centroid, allowing for the identification of underlying patterns and relationships within the data. This technique is particularly useful when the goal is to categorize data points, facilitating the understanding of data distributions.

Principal Component Analysis (PCA) complements K-means clustering by serving as a dimensionality reduction technique. It simplifies the complexity of high-dimensional datasets while retaining the most significant variance components. By transforming the data into a lower-dimensional space, PCA helps to highlight the essential patterns and structures in the data, making it easier to visualize and interpret.

In combination, these two techniques enable data scientists and analysts to uncover meaningful insights from large datasets, effectively identifying trends, clusters, and patterns that may not be evident in the original high-dimensional data. The synergy between clustering and dimensionality reduction is critical in various AI applications, such as image recognition and market segmentation.

The other options presented do not effectively contribute to discovering patterns. Overfitting refers to a modeling