Difference between Supervised and Unsupervised Learning in Data Mining

In data mining, supervised learning involves training a model using labeled data where the algorithm aims to learn the relationship between input features and target outputs. This type of learning requires a clear understanding of the data labels and is used for classification and regression tasks.

On the other hand, unsupervised learning does not require labeled data and the algorithm tries to find patterns and relationships in the data without predefined outputs. Unsupervised learning is used for clustering and association tasks, where the goal is to discover the underlying structure of the data.

Therefore, the main difference between supervised and unsupervised learning lies in the availability of labeled data and the objective of the learning process.