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introduction-3

introduction-3 - CS345 Data Mining Course Introduction...

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1 CS345 --- Data Mining Course Introduction Varieties of Data Mining Bonferroni’s Principle
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2 Course Staff rhombus6 Instructors : rhombus4 Anand Rajaraman rhombus4 Jeff Ullman rhombus6 TA : rhombus4 Babak Pahlavan
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3 Requirements rhombus6 Homework (Gradiance and other) 20% rhombus4 Gradiance class code B0E9AA66 rhombus4 Note URL for class: www.gradiance.com/ services (not /pearson). rhombus6 Project 40% rhombus6 Final Exam 40%
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4 Project rhombus6 Software implementation related to course subject matter. rhombus6 Should involve an original component or experiment. rhombus6 More later about available data and computing resources.
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5 Team Projects rhombus6 Working in pairs OK, but … 1. We will expect more from a pair than from an individual. 2. The effort should be roughly evenly distributed.
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6 What is Data Mining? rhombus6 Discovery of useful, possibly unexpected, patterns in data. rhombus6 Subsidiary issues: rhombus4 Data cleansing : detection of bogus data. E.g., age = 150. Entity resolution. rhombus4 Visualization : something better than megabyte files of output. rhombus4 Warehousing of data (for retrieval).
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7 Cultures rhombus6 Databases : concentrate on large-scale (non-main-memory) data. rhombus6 AI (machine-learning): concentrate on complex methods, small data. rhombus6 Statistics : concentrate on models.
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8 Models vs. Analytic Processing rhombus6 To a database person, data-mining is an extreme form of analytic processing -- queries that examine large amounts of data. rhombus4 Result is the data that answers the query.
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