1. Data Mining & Machine Learning Group Ch. EickAssignment 5 Assignment5 Topics 1. A Survey on Recent OLAP/Data Warehousing Tools Revanth Anireddy and Deepthi Mantha Dec. 1 2. A Survey on Graph Mining Tools and Algorithms Xi Zhu and Meenakshi Sharma 3. Uses of Data Mining in Bio-Informatics Rohith Kumar Kodakandla and Rajesh Reddy Konatham 4. The PAGERANK Algorithm and its Successors ( Subramanya Chaitanya and Kandala Kaavya Karanam) & (Charlotte Lloren, Ruchika Bhatia, and Ricardo Mauricio) 5. A Survey on Ensemble Methods for Prediction and/or Classification Dat Chu, Yen Le, and Emil Ismailov 6. A Survey on non-Apriori-style Association Rule Mining Algorithms Karthik Vangala and Sayan Bardhan 7. A Survey on Spatial Measures of Interestingness and Tools to Use them Anurag Nagar and Mustafa Sherazi 8. Using Data Mining for Analyzing Satellite Images of Earth Sara Chaarawi and Waleed Faris

2. Data Mining & Machine Learning Group Ch. EickAssignment 5 Assignment5 Details  The project is a group project; take advantage of your increased man-power and subdivide work between group members intelligently.  Groups submit a 9-11 (12-14; for groups with 3 students) page (single-spaced) report and give a 10 (15) minute presentation on December 1 (one or two), or December 3, 2009.  Most Topics require a web search; include a summary of the most recent development in the field of your investigation in your report and presentation.  Each group should meet Dr. Eick once during his office hour either in the Nov. 9 or Nov. 16 week.  The report should follow the “traditional” organization: Introduction- …(Main_Part)-Summary-References  Each group member is expected to participate in the group’s presentation.  Presentations are uploaded at 9:55a on Dec 1/3!

3. Data Mining & Machine Learning Group Ch. EickAssignment 5 Assignment5 Schedule of Presentations 9:55a: Upload Transparencies; please, don’t be late!! 1. A Survey on Recent OLAP/Data Warehousing Tools Revanth Anireddy and Deepthi Mantha Dec. 1, 10:10a 2. A Survey on Graph Mining Tools and Algorithms Xi Zhu and Meenakshi Sharma Dec. 3, 10:10a 3. Uses of Data Mining in Bio-Informatics Rohith Kumar Kodakandla and Rajesh Reddy Konatham Dec. 3, 10:20a 4. The PAGERANK Algorithm and its Successors ( Subramanya Chaitanya and Kandala Kaavya Karanam Dec. 3, 10:30a ) & (Charlotte Lloren, Ruchika Bhatia, and Ricardo Mauricio Dec. 1, 10:20a ) 5. A Survey on Ensemble Methods for Prediction and/or Classification Dat Chu, Yen Le, and Emil Ismailov Dec. 3, 10:45a 6. A Survey on non-Apriori-style Association Rule Mining Algorithms Karthik Vangala and Sayan Bardhan Dec. 3, 11:00a 7. A Survey on Spatial Measures of Interestingness and Tools to Use them Anurag Nagar and Mustafa Sherazi Dec. 3, 11:15a 8. Using Data Mining for Analyzing Satellite Images of the Earth Sara Chaarawi and Waleed Faris Dec. 3, 11:25a

4. Data Mining & Machine Learning Group Ch. EickAssignment 5 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.

5. Data Mining & Machine Learning Group Ch. EickAssignment 5 Thoughts on Presentations2  Establish goals for your presentation --- what is / are the 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.

6. Data Mining & Machine Learning Group Ch. EickAssignment 5 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.

7. Data Mining & Machine Learning Group Ch. EickAssignment 5 Presentation 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 identifyyourself with the contents of your presentation --- you have something important to tell! Try to convice the audience!  Try to entertain!