Advanced Structure Discovery: Distance Metrics and Time Series Data

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Description

This lecture covers advanced structure discovery techniques, including clustering algorithms like K-Means, Spectral, and Hierarchical Agglomerative Clustering. It delves into distance/similarity measures for time series data, such as Euclidean, Jaccard, Hamming, and Earth Mover distances. The lecture also explores similarity measures like Cosine and Gaussian Kernel with Euclidean distance, along with practical examples of clustering student behaviors and engagement patterns in MOOCs.

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