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Welcome! DragonStar 2010: Data Mining and Appl. To Sensor Networks

Welcome! DragonStar 2010: Data Mining and Appl. To Sensor Networks. Qiang Yang, Yunhao Liu Hong Kong University of Science and Technology qyang@cs.ust.hk http://www.cs.ust.hk. KDDCUP from past years 2007: Predict if a user is going to rate a movie?

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Welcome! DragonStar 2010: Data Mining and Appl. To Sensor Networks

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  1. Welcome!DragonStar 2010: Data Mining and Appl. To Sensor Networks Qiang Yang, Yunhao Liu Hong Kong University of Science and Technology qyang@cs.ust.hk http://www.cs.ust.hk Course Introduction

  2. KDDCUP from past years 2007: Predict if a user is going to rate a movie? Predict how many users are going to rate a movie? 2006: Predict if a patient has cancer from medical images 2005: Given a web query (“Apple”), predict the categories (IT, Food) 1998: Given a person, predict if this person is going to donate money In general, we wish to Input: Data Output: Build model Apply model to future data Data Mining: An Example Course Introduction 2

  3. Data Mining: Convergence of Three Technologies Course Introduction 3

  4. Definition: Predictive Model • A “black box” that makes predictions about the future based on information from the past and present • Large number of inputs usually available Course Introduction 4

  5. How are Models Built and Used? • High Level View: Course Introduction 5

  6. What does the Real World Look Like Course Introduction 6

  7. Foundations Classification Models Clustering Models Feature Selection Advanced Topics Link prediction Transfer Learning Query analysis Applications Web Wireless Application to Sensor Nets Sensor Net Basics Application Examples Student Projects Weka applications Presentations Panel Discussions Data mining research Student Q&A This Course… Course Introduction

  8. Course Description • Data Mining and Knowledge Discovery • Focus: • Focus 1: Theoretical foundations in Pattern Recognition and Machine Learning • Algorithms: • Differences? • where they apply? • Focus 2: Survey of recent research • Focus 3: Hands-on, apply algorithms to KDD data sets Course Introduction

  9. Hands On • Software: Weka (Matlab) • Projects: • Data Sets • Projects  Research Topics • Presentation (Friday) Course Introduction

  10. Textbooks (DM) • Textbooks: For reference only • Data Mining -- Concepts and Techniques by Jiawei Han and Micheline Kamber. Morgan Kaufmann Publishers. • Introduction to Data Mining by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, Pearson International Edition, 2005. • Data Mining.  by Ian Witten and Ebe Frank. Course Introduction

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