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A Divide-and-Merge Methodology for Clustering

This study introduces a novel divide-and-merge technique combining top-down and bottom-up clustering methods to generate hierarchies and flat clusters effectively. The authors propose a spectral algorithm for the divide phase and dynamic programming for the merge phase. Experimental evaluations show promising results on real-world data.

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A Divide-and-Merge Methodology for Clustering

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  1. A Divide-and-Merge Methodology for Clustering Advisor : Dr. Hsu Presenter : Hsin-Yi Huang Authors : David Cheng, Ravi Kavnnan, Santosh Vempala and Grant Wang 2007.TODS.27

  2. Outline • Motivation • Objective • Methodology • Divide Phase • Merge Phase • Application • Experiment • Conclusion • Comments

  3. Motivation • Previous algorithms use either top-down or bottom-up methods to construct a hierarchical clustering. • Others produce a flat clustering using local search (e.g., k-mean).

  4. Objective • The authors present a divide-and-merge methodology that combines top-down and bottom-up techniques to create both a hierarchy and a flat clustering. divide merge

  5. Divide Phase • For the divide phase, the authors suggest an efficient spectral algorithm. divide phase

  6. Merge Phase • The authors are trying to maximize the objective function g, the dynamic program will find a clustering COPT-TREE in the tree. error the number of clusters

  7. Merge Phase (cont.) • K-means • Min-Diameter • Min-Sum • Correlation Clustering

  8. Application

  9. Application (cont.) • The authors implemented the methodology in a meta-search engine, and the web site is located at http://eigencluster.csail.mit.edu • The divide phase: spectral algorithm • The merge phase: relaxed correlation clustering The dissimilarity within a cluster The amount of similarly the clustering fails to capture

  10. Experiment • F-Measure • Entropy • Accuracy • Confusion Matrix The columns are the classed Cj The rows are the clusters

  11. Experiment (cont.) • Divide Phase • Reuters • Merge Phase

  12. Conclusion • The authors present a divide-and-merge methodology for clustering. • An efficient and effective spectral algorithm for the divide phase. • For the merge phase, a dynamic programming formulations that compute the optimal tree-respecting clustering for standard objective functions. • The author propose a thorough experimental evaluation of the methodology shows that technique is effective on real-world data.

  13. Comments • Advantage • an interesting idea • Drawback • … • Application • clustering

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