Your SlideShare is downloading. ×
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
B llabs
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

B llabs

373

Published on

0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
373
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
7
Comments
0
Likes
1
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide
  • Let pi,j be the rate at which word i occurs in document j, and pj be the average across documents( sum Pij/ndocs)The size of each word is mapped to its maximum deviation ( maxi(pi,j- pj ) ), and its angular position is determined by the document where that maximum occurs.
  • Transcript

    • 1. Tools & techniquesfor working withdatasets Tony Hirst Dept of Communication and Systems The Open University
    • 2. Quick wins andhalf-hour hacks
    • 3. Building atoolbox…
    • 4. http://mashe.hawksey.info/2012/11/mining-and-openrefineing-jiscmail-a-look-at-oer-discuss//via Martin Hawksey/@mhawksey
    • 5. “You can quickly create an online 3-Dvisualisation (with Google Earth) ofthese rare documents”
    • 6. R-Studio
    • 7. All at once orone at a time?
    • 8. Macroscopes
    • 9. @mediaczar (Accession Plot)
    • 10. Google Maps, 1884 edition?
    • 11. Overview first, zoom and filter, then details-on-demandFrom: The Eyes Have It:A Task by Data Type Taxonomy for Information Visualizations
    • 12. • X and Y (at a push, Z)• Node size and colour• (Node label size and colour)• Edge thickness and colour• (Edge label and colour)• Node proximity/grouping• Clustering• Filtering and differential application of the above
    • 13. Group by  Hierarchy inside(implied) containment
    • 14. Treemap in R
    • 15. Similarities anddifferences
    • 16. Single page app + linkage
    • 17. Templated data views
    • 18. blog.ouseful.info @psychemedia

    ×