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Unsupervised Learning: Clustering Some material adapted from slides by Andrew Moore, CMU. Visit http://www.autonlab.org/tutorials/ for http://www.autonlab.org/tutorials/ Andrew’s repository of Data Mining tutorials.

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Unsupervised Learning Supervised learning used labeled data pairs (x, y) to learn a function f : X→Y. Supervised learning used labeled data pairs (x, y) to learn a function f : X→Y. But, what if we don’t have labels? But, what if we don’t have labels? No labels = unsupervised learning No labels = unsupervised learning Only some points are labeled = semi-supervised learning Only some points are labeled = semi-supervised learning Labels may be expensive to obtain, so we only get a few. Labels may be expensive to obtain, so we only get a few. Clustering is the unsupervised grouping of data points. It can be used for knowledge discovery. Clustering is the unsupervised grouping of data points. It can be used for knowledge discovery.

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Clustering Data

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K-Means Clustering K-Means ( k, data ) Randomly choose k cluster center locations (centroids). Loop until convergence Assign each point to the cluster of the closest centroid. Reestimate the cluster centroids based on the data assigned to each.

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K-Means Clustering K-Means ( k, data ) Randomly choose k cluster center locations (centroids). Loop until convergence Assign each point to the cluster of the closest centroid. Reestimate the cluster centroids based on the data assigned to each.

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K-Means Clustering K-Means ( k, data ) Randomly choose k cluster center locations (centroids). Loop until convergence Assign each point to the cluster of the closest centroid. Reestimate the cluster centroids based on the data assigned to each.

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K-Means Animation Example generated by Andrew Moore using Dan Pelleg’s super- duper fast K-means system: Dan Pelleg and Andrew Moore. Accelerating Exact k-means Algorithms with Geometric Reasoning. Proc. Conference on Knowledge Discovery in Databases 1999.

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Problems with K-Means Very sensitive to the initial points. Very sensitive to the initial points. Do many runs of k-Means, each with different initial centroids. Do many runs of k-Means, each with different initial centroids. Seed the centroids using a better method than random. (e.g. Farthest-first sampling) Seed the centroids using a better method than random. (e.g. Farthest-first sampling) Must manually choose k. Must manually choose k. Learn the optimal k for the clustering. (Note that this requires a performance measure.) Learn the optimal k for the clustering. (Note that this requires a performance measure.)

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Problems with K-Means How do you tell it which clustering you want? How do you tell it which clustering you want? Constrained clustering techniques Constrained clustering techniques Same-cluster constraint (must-link) Different-cluster constraint (cannot-link)

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