Clustering and Association Rule Mining

Clustering and Association Rule Mining are advanced techniques used in data mining to uncover patterns and relationships within datasets. In the Clustering portion of this session, participants will learn about different clustering algorithms, such as K-means and hierarchical clustering, and how to apply them to group similar data points together. They will also explore practical applications of clustering, such as customer segmentation and anomaly detection.In the Association Rule Mining portion, participants will delve into the theory and practice of discovering interesting relationships between variables in large datasets. They will learn about popular algorithms like Apriori and FP-growth, and how to interpret and apply the resulting rules to make data-driven decisions.Through hands-on exercises and real-world examples, participants will gain a deep understanding of these powerful techniques and how to leverage them to extract valuable insights from their data.

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