introduction

introduction - CS345 - Data Mining Introductions What Is...

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1 CS345 --- Data Mining Introductions What Is It? Cultures of Data Mining
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2 Course Staff r Instructors: R Anand Rajaraman R Jeff Ullman r TA: R Robbie Yan
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3 Requirements r Homework (Gradiance and other) 20% R Gradiance class code BB8F698B r Project 40% r Final Exam 40%
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4 Project r Software implementation related to course subject matter. r Should involve an original component or experiment. r We will provide some databases to mine; others are OK.
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5 Team Projects r 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? r Discovery of useful, possibly unexpected, patterns in data. r Subsidiary issues: R Data cleansing : detection of bogus data. E.g., age = 150. R Visualization : something better than megabyte files of output. R Warehousing of data (for retrieval).
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7 Typical Kinds of Patterns 1. Decision trees : succinct ways to classify by testing properties. 2. Clusters : another succinct classification by similarity of properties. 3. Bayes, hidden-Markov , and other statistical models, frequent-itemsets : expose important associations within data.
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8 Example: Clusters x x x x x x x x x x x x x x x x xx x x x x x x x x x x x x x x x x x x x x x x x
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introduction - CS345 - Data Mining Introductions What Is...

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