Transcript Mining Regional Knowledge in Spatial Dataset

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COSC 6335 Project4 Fall 2011
Project4 is a group project (we will have 7 groups of 4 students and 1 group of 3
students). The goal of Project4 is to give a timed 9-minute presentations “about
something interesting in data mining” and to summarize your findings in a (1.5 to
2.5 single spaced page) report .
The report should follow the “traditional” organization: Introduction…(Main_Part)-Summary-References (references do not count towards the page
limit; no abstract); alternatively, you can follow the format of a longer news
paper article (e.g. New York Times)
Project 4 Student Presentations are scheduled for Thursday, November 17, 1011:30a; each student should participate in the presentation, except evaluators
might not be presenting. Project Reports are due on Sa., November 19, 11p
(electronic submission). Presentation slides (for details see next slide) have to
be submitted by Tuesday, November 17, 2p.
The course grade will be based on
1. The interestingness of the project
2. The content of the presentation and the report
3. The form of the presentation and the report
Presentations will be evaluated by the audience. The project reports will be
graded by Zechun and Dr. Eick.
Ch. Eick
Data Mining & Machine Learning Group
Project 4 2011
COSC 6335 Project4 Presentations
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Presentation slides have to be submitted by Tuesday, November 15, 2p to
Zechun—this is a hard deadline!; presentations should be timed 9-minute
presentations (no more no less); followed by a 1-minute last slide which
carries the title (Evaluate GroupX, please!). The slides of all 8 groups will be
concatenated to a 80 minute, un-interruptible event which will come to a
conclusion exactly 80 minutes after it started.
Presentations will be evaluated by the other groups on a 1-10 scale
(10=exceptional…1=very very bad). Typically, a score of 6 should be given
to a middle of the field performance, but evaluators should not be hesitant to
give 10’s, 9’s, 2’s, and 1’s, if appropriate.
1. The interestingness of the project
2. The content of the presentation
3. The form of the presentation (is it clear and understandable, how
appealing are the slides,…)
Each group should name by 11/15/10a one of its members who does the
evaluation of group presentations. We will use clickers for the evaluation.
Privacy: We will post the scores and average scores of each group, but which
group gave which score to the presenting group will not be revealed!
Ch. Eick
Data Mining & Machine Learning Group
Project 4 2011
Thoughts on Presentations
 speak loudly and freely --- do not read!
 make a plan for your presentation.
 Give a brief overview of your presentation at the
beginning
 Introduce the topic of your presentation clearly.
 In general, a presentation consists of: introduction,
main-part, conclusion.
 Finish your presentation with a conclusion that
summarizes your results/findingsnever skip the
conclusion.
Ch. Eick
Data Mining & Machine Learning Group
Project 4 2011
Thoughts on Presentations2
 Establish goals for your presentation --- what is / are the
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message / messages of your presentation?
Prepare the presentation taking the viewpoint of a person
that will listen to your presentation.
Make a "proud presentation" --- if you aren't, pretend to be
proud.
Interact with the audience; keep the audience awake (make
a joke,
Tell a story, challenge / tease / reward / punish / surprise the
audience, use funny examples, ask questions.
Try to refer to previous presentations.
Establish contexts and context shifts clearly.
Don't get lost in technical detailsunless they are important
for the message of your talk.
Ch. Eick
Data Mining & Machine Learning Group
Project 4 2011
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Presentations Part3
If you get completely lost in your presentation --- take a deap
breath pause for a 20 seconds, and continue (?!?).
Use transparencies and/or the blackboard.
Do not write too much on a transparency (about 5-12 lines;
does not apply to examples). Use large fonts.
Use Large Fonts! Use Color!!
Unreadable transparencies are unacceptable! Don't put
unrelated things on the same transparency!
Use examples; general descriptions of algorithms or concepts
are very hard to understand.
A picture is worth more than 1000 words!!
Answer questions politely! You need not to answer questions
immediately. Don't let questions mess up your presentation.
You are allowed to postpone answering questions.
Ch. Eick
Data Mining & Machine Learning Group
Project 4 2011
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Presentations Part4
Important things should be said more than once.
Take your time --- do not hurry through your presentation (unless
near the end).
Make a schedule for your presentation; check the schedule during
your presentation. Subdivide your presentation into mandatory parts
and optional parts (parts that can be skipped if you run out of time).
Practice your presentation --- entertain your cat / grandmother /
Don't stand in front whatever you present.
Keep eye-contact with the audience! Try to read the audience
reaction to what you are presenting and use this know for the
remainder/next presentation.
Smile from time to time --- this is not a funeral!
Be emotional in the sense that the audience feels that you identify
yourself with the contents of your presentation --- you have
something important to tell! Try to convice the audience!
Try to entertain!
Ch. Eick
Data Mining & Machine Learning Group
Project 4 2011