1. Visualization and Cluster

2. Visual Analysis
“the science if analytical reasoning facilitated by interactive visual interface” (Thomas and Cook, 2005)
Interact with data
Test hypotheses
Formulate knowledge
Human intuitive is reliable
Few user are well-versed in algorithms

4. Clustering without Human
User have domain knowledge for feature selection
Which feature is more important than which
Sometimes feature have different weight in different use scenarios
Priority Distribution
User know outliers in dataset
User can give initial state

5. Show Cluster with Visualization

6. BaVA: Approach Bayesian Visual Analytics framework
Display posterior result
Dimension Reduction for display
Expert give feedback through adjust layout
Feedback as observation level rather than dimension level
What kind of interaction should be captured
Human input change the underlying probabilistic model and updates the display

7. Semantic Layout ForceSPIRE

8. Interface design Move Document link document: Pin: semantic location
Update position based on current weight
link document:
need update the weight
Pin: semantic location
Exp: move document close to a pin is expressive movement
Text highlighting
Increase the weight for highlighted keywords and update the layout

9. Interface design Search Document Coloring Visual level of detail
Document contain the keywords will be highlighted
Document Coloring
Mark document of same group
Visual level of detail
More detail = easier to reference
Annotation
Give semantic information to clusters = easier to reference

10. Interaction Feedback Spatial information on interaction is ambiguity
Will cover it later
Operations are on observation level, not on dimension level
Let user adjust parameters directly is just guessing game
Also, operation is in 2D space rather than high dimension space

11. Interaction Feedback
Two examples: PPCA and MDS

12. Probabilistic PCA PCA: minimize the variance e Problem of PCA:
Important structures in data may not correlate with variance, like cluster
Probabilistic PCA

13. Probabilistic PCA Let , the marginal variance of d
Sd the empirical variance of d
MAP(∑d) = Sd
Let
The coordinate
The relationship between variance and coordinate

14. User guided PPCA Display show point layout in 2D space
Drag away or drag close two observations if user thinks they are close

15. User guided PPCA Dragged points have different and similar features
A hypothetical variance matrix f(p)
An addition weight v to show how important this adjust is
Parameter feedback

16. User guided PPCA

17. Weighted MDS
Minimize the difference if sum of distance in the real space and in the embedded space

18. User guided MDS

19. User guided MDS User adjust the relative position of points
Solve w so that
r: adjust position, d: original position

20. V2PI Visual to parametric Interaction
Spatial Interaction is intuitive but ambiguity
Move a point can means:
Move toward to a unmoved points
Move around and happen to be moved closer to unmoved points
Need better explicit interaction
Use tag to distinguish

21. Interaction Design For each interaction, data are involved in two ways
Explicit
Implicit
Example: move A to B, A is Explicitly involved and B is implicitly involved
For unmoved points:
It is implicitly involved
Not involved at all (ignored by user)
Get meaning od unmoved point with minimal addition effort

22. Interaction Design Moved set
Highlighted set (user highlight some points that he found interesting)
Untouched set (ignored by user)

23. Pair-wise weighting C_ij show user’s preference on each data C_ij:
Combine value is 1

24. Pair-wise weighting
n_h and n_m are the number of object of highlighted and moved in the dataset

25. User guided DR Key notes:
We can rely on user’s domain knowledge to improve Dimension Reduction
Data manipulating must be intuitive and efficient