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Structuring Interactive Cluster Analysis

Structuring Interactive Cluster Analysis. Wayne Oldford University of Waterloo. Overview. Argument:. Content by example:. ill-defined problem high-interaction desirable explore partitions recast algorithms. problems resources interactive clustering partition moves implications.

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Structuring Interactive Cluster Analysis

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  1. Structuring Interactive Cluster Analysis Wayne Oldford University of Waterloo Dept. of Computer Science Memorial University of Newfoundland

  2. Overview Argument: Content by example: • ill-defined problem • high-interaction desirable • explore partitions • recast algorithms • problems • resources • interactive clustering • partition moves • implications Dept. of Computer Science Memorial University of Newfoundland

  3. Problem … geometric/visual structure Dept. of Computer Science Memorial University of Newfoundland

  4. Problem … context matters Dept. of Computer Science Memorial University of Newfoundland

  5. Problem … structure in context … segmentation in MRI … image source Dept. of Computer Science Memorial University of Newfoundland

  6. Problem … context specific structure … image source Dept. of Computer Science Memorial University of Newfoundland

  7. Problem … some specific some not … image source Dept. of Computer Science Memorial University of Newfoundland

  8. Problem … some specific some not … image source Dept. of Computer Science Memorial University of Newfoundland

  9. Problem • Find groups in data • Similar objects are together • Groups are separated • What do you mean similar? • Problem is ill defined: • E.g. what is contiguous structure? • When are groups separate? • Can we believe it? Dept. of Computer Science Memorial University of Newfoundland

  10. Computational resources 1. Processing 2. Memory 3. Display Balance and integrate Dept. of Computer Science Memorial University of Newfoundland

  11. High interaction • multiple displays • integrate computational resources • software design? Dept. of Computer Science Memorial University of Newfoundland

  12. Example: image analysis Dept. of Computer Science Memorial University of Newfoundland

  13. Example: context and function plots Dept. of Computer Science Memorial University of Newfoundland

  14. Example: mutual support and shapes Dept. of Computer Science Memorial University of Newfoundland

  15. Example: exploratory data analysis Dept. of Computer Science Memorial University of Newfoundland

  16. Interactive clustering • visual grouping • location, motion, shape, texture, ... • linking across displays • manual • selection • cases, variates, groups, ... • colouring • focus • immediate and incremental • context can be used to form groups • multiple partitions Dept. of Computer Science Memorial University of Newfoundland

  17. Automated clustering: typical software • resources dedicated to numerical computation • teletype interaction • runs to completion • graphical “output” • don’t always work so well (no universal solution) • confirm via exploratory data analysis Must be integrated with interactive methods Dept. of Computer Science Memorial University of Newfoundland

  18. Example: K-means clustering Dept. of Computer Science Memorial University of Newfoundland

  19. Example: VERI Visual Empirical Regions of Influence join points if no third point falls in this region Dept. of Computer Science Memorial University of Newfoundland

  20. Example: VERI Dept. of Computer Science Memorial University of Newfoundland

  21. Integrating automatic methods: Move about the space of partitions: Pa --> Pb --> Pc --> …. Which operators f f(Pa) --> Pb are of interest? Dept. of Computer Science Memorial University of Newfoundland

  22. Refine Reduce Dept. of Computer Science Memorial University of Newfoundland

  23. Reassign Dept. of Computer Science Memorial University of Newfoundland

  24. -> 2 -> 3 -> 4 -> 5 Refinement sequence: 1 Dept. of Computer Science Memorial University of Newfoundland

  25. -> 5 Reassign, reduce sequence: 5 Dept. of Computer Science Memorial University of Newfoundland

  26. -> 5 -> 4 -> 3 -> 2 Reassign, reduce sequence: 5 Dept. of Computer Science Memorial University of Newfoundland

  27. Moves: examples: • refine (Pold) --> Pnew break minimal spanning tree • reduce (Pold) --> Pnew join near centres • reassign (Pold) --> Pnew k-means maximize F • partition (graphic) --> Pnew colours from point cloud Dept. of Computer Science Memorial University of Newfoundland

  28. Challenges: • varying focus • subsets (selected manually and at random) • merging new data into partition • exploring multiple partitions • interactive display and comparison • resolving many to one • interface design • control panels, options • interaction Dept. of Computer Science Memorial University of Newfoundland

  29. Interface - reduce Dept. of Computer Science Memorial University of Newfoundland

  30. Interface - refine Dept. of Computer Science Memorial University of Newfoundland

  31. Interface - reassign Dept. of Computer Science Memorial University of Newfoundland

  32. Interaction Dept. of Computer Science Memorial University of Newfoundland

  33. Interaction - refine 2 Dept. of Computer Science Memorial University of Newfoundland

  34. Interaction - refine 3 Dept. of Computer Science Memorial University of Newfoundland

  35. Interaction -save partition movie Dept. of Computer Science Memorial University of Newfoundland

  36. Interaction -refine 4 Dept. of Computer Science Memorial University of Newfoundland

  37. Interaction - refine 5 Dept. of Computer Science Memorial University of Newfoundland

  38. Interaction - refine 5 dendrogram Dept. of Computer Science Memorial University of Newfoundland

  39. Interaction - reassign Dept. of Computer Science Memorial University of Newfoundland

  40. Interaction - cluster plot movie Dept. of Computer Science Memorial University of Newfoundland

  41. Implications: • Algorithms (re)cast in terms of moves: • refine, reduce • reassign • partition, partition-path • easily understandable (e.g. geometric structures) • specify required data structures • e.g. ms tree, triangulation, var-cov matrix, … Dept. of Computer Science Memorial University of Newfoundland

  42. New problems: • interface design • multiple partitions • comparison and/or resolution • multiple display • inference Dept. of Computer Science Memorial University of Newfoundland

  43. Summary • Cluster analysis is naturally exploratory and needs integration with modern interactive data analysis. • Enlarging the problem to partitions: • simplifies and gives structure • encourages exploratory approach • integrates naturally • introduces new possibilities (analysis and research) Dept. of Computer Science Memorial University of Newfoundland

  44. Acknowledgements: • Catherine Hurley, Erin McLeish, Rayan Yahfoufi, Natasha Wiebe • U(W) students in statistical computing • Quail: Quantitative Analysis in Lisp http://www.stats.uwaterloo.ca/Quail Dept. of Computer Science Memorial University of Newfoundland

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