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Data Clustering and Mining

Data Clustering and Mining. Dexin Zhou – Bard College (Presenter) Ralph Abbey – North Carolina State U. Jeremy Diepenbrock – Washington U. at St. Louis Project Advisor: Dr. Carl Meyer Additional Advising: Dr. Amy Langville Graduate Assistant: Shaina Race. What is Data Clustering?.

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Data Clustering and Mining

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  1. Data Clustering and Mining Dexin Zhou – Bard College (Presenter) Ralph Abbey – North Carolina State U. Jeremy Diepenbrock – Washington U. at St. Louis Project Advisor: Dr. Carl Meyer Additional Advising: Dr. Amy Langville Graduate Assistant: Shaina Race

  2. What is Data Clustering? • Clustering is the partitioning of a data set into subsets (clusters). • We are interested in creating good clusters that allow us to reorganize disordered data into a block structureso that useful information can be extracted.

  3. A Visible Example Before Clustering After Clustering

  4. What are we clustering? • An 86 mini-document set that we created with 13 topics • A 185 document set used in Daniel Boley’s paper with 10 topics • SAS grocery store dataset

  5. Preparing the data • Term Aij is in the following form • g term is a function of term i, it downplays the terms that appear frequently globally • l term is a function of the raw frequency of a certain term in document j(eg: log) • d term is a normalization factor

  6. How? • Principal Direction Divisive Partitioning • Principal Direction Gap Partitioning • Non-Negative Matrix Factorization • Clustering Aggregation

  7. Singular Value Decomposition

  8. Principle Direction Divisive Partitioning

  9. PDDP

  10. PDDP

  11. Principle Direction Gap Partitioning Plot of the Second Right Singular Vector Plot of the First Right Singular Vector Sorted Value Sorted Value Sorted Indices Sorted Indices

  12. A Comparison of PDGP w/ PDDP

  13. Centering Vs. Non-Centering

  14. Non-Negative Matrix Factorization

  15. NMF Clustering

  16. Cluster Aggregation

  17. Cluster Aggregation

  18. Metrics • Entropy Method • A standard measurement based on our prior knowledge to the data file. • Density Method • Does not require prior knowledge to the data file. • Less accurate.

  19. Mini-document dataset

  20. Mini-document dataset Result

  21. Boley’s J1 Dataset

  22. Boley’s Dataset Result

  23. SAS Grocery Dataset

  24. SAS Grocery Dataset Results

  25. SAS Grocery Dataset Result

  26. Conclusion

  27. For Additional Information • Please Visit • http://meyer.math.ncsu.edu/Meyer/REU/REU.html

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