can describethe ideabehind K-meansclusteringknowsdifferencebetweencomplete andpartialclusteringAsked aquestion thepresenterdidn't knowthe answer todidn't checkemail duringentirepresentationdidn't usephone duringentirepresentationfinds amistake inthe slideAsked aquestion thatconfusedeverybodydidn'tfallasleep!Gave ausefulexample toexplain aconceptCan namethree basicclusteringalgorithmscan givea reasonfor usingclusteringanalysisclapped atthe end ofpresentationparticipatedin at leastonediscussionAsked aquestion thepresenterdidn't knowthe answer toReadingmaterialshaveannotationsand highlightsAnswered aquestion thepresenteraskedCORRECTLYSaid helloto onlinestudentsAnswered aquestion thepresenteraskedINCORRECTLYcan statedifference betweenexclusive,overlapping, andfuzzy clusteringknows how tochoose thenumber ofclusters in K-meansclusteringcan describeand drawthreedifferent typesof clusterscan draw thedifferencebetweenhierarchical &partitionalclusteringAsked afollow upquestionto aquestionBrought thereadingmaterialsto classcan describethe ideabehind K-meansclusteringknowsdifferencebetweencomplete andpartialclusteringAsked aquestion thepresenterdidn't knowthe answer todidn't checkemail duringentirepresentationdidn't usephone duringentirepresentationfinds amistake inthe slideAsked aquestion thatconfusedeverybodydidn'tfallasleep!Gave ausefulexample toexplain aconceptCan namethree basicclusteringalgorithmscan givea reasonfor usingclusteringanalysisclapped atthe end ofpresentationparticipatedin at leastonediscussionAsked aquestion thepresenterdidn't knowthe answer toReadingmaterialshaveannotationsand highlightsAnswered aquestion thepresenteraskedCORRECTLYSaid helloto onlinestudentsAnswered aquestion thepresenteraskedINCORRECTLYcan statedifference betweenexclusive,overlapping, andfuzzy clusteringknows how tochoose thenumber ofclusters in K-meansclusteringcan describeand drawthreedifferent typesof clusterscan draw thedifferencebetweenhierarchical &partitionalclusteringAsked afollow upquestionto aquestionBrought thereadingmaterialsto class

Clustering and Community Detection - Call List

(Print) Use this randomly generated list as your call list when playing the game. There is no need to say the BINGO column name. Place some kind of mark (like an X, a checkmark, a dot, tally mark, etc) on each cell as you announce it, to keep track. You can also cut out each item, place them in a bag and pull words from the bag.


1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
  1. can describe the idea behind K-means clustering
  2. knows difference between complete and partial clustering
  3. Asked a question the presenter didn't know the answer to
  4. didn't check email during entire presentation
  5. didn't use phone during entire presentation
  6. finds a mistake in the slide
  7. Asked a question that confused everybody
  8. didn't fall asleep!
  9. Gave a useful example to explain a concept
  10. Can name three basic clustering algorithms
  11. can give a reason for using clustering analysis
  12. clapped at the end of presentation
  13. participated in at least one discussion
  14. Asked a question the presenter didn't know the answer to
  15. Reading materials have annotations and highlights
  16. Answered a question the presenter asked CORRECTLY
  17. Said hello to online students
  18. Answered a question the presenter asked INCORRECTLY
  19. can state difference between exclusive, overlapping, and fuzzy clustering
  20. knows how to choose the number of clusters in K-means clustering
  21. can describe and draw three different types of clusters
  22. can draw the difference between hierarchical & partitional clustering
  23. Asked a follow up question to a question
  24. Brought the reading materials to class