Clustering is a technique used in data mining to identify patterns and group similar data points together. It helps in understanding the inherent structure or relationships within the data, without any prior knowledge of the groups. For example, a retail company can use clustering to categorize its customers into different segments based on their purchasing behavior. This enables targeted marketing strategies for each customer segment, resulting in improved sales and customer satisfaction.

Association rule mining, on the other hand, focuses on discovering relationships and dependencies within datasets. It aims to identify frequently occurring patterns or associations between different items or variables. For instance, association rule mining can be applied in market basket analysis, where relationships between products frequently purchased together are identified. This information can be utilized to optimize product placement in stores or suggest related products to customers during online shopping.

In both clustering and association rule mining, the output generated can provide valuable insights and drive data-driven decision-making. These techniques are widely used across various domains, including marketing, healthcare, and finance.