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Machine Learning with WEKA

Discover how to effectively apply clustering techniques using WEKA Explorer to identify groups of similar instances in your datasets. This tutorial covers various clustering algorithms including k-Means, EM, Cobweb, X-means, and FarthestFirst. Learn how to visualize clusters and compare them against true clusters if available. Additionally, we will discuss evaluation metrics, particularly focusing on log-likelihood for clustering schemes that output probability distributions. Enhance your machine learning skills and improve your data analysis with WEKA!

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Machine Learning with WEKA

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  1. Machine Learning with WEKA

  2. Explorer: clustering data • WEKA contains “clusterers” for finding groups of similar instances in a dataset • Implemented schemes are: • k-Means, EM, Cobweb, X-means, FarthestFirst • Clusters can be visualized and compared to “true” clusters (if given) • Evaluation based on loglikelihood if clustering scheme produces a probability distribution University of Waikato

  3. University of Waikato

  4. University of Waikato

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  10. University of Waikato

  11. University of Waikato

  12. University of Waikato

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  14. University of Waikato

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  17. University of Waikato

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