2015/6/1Course Introduction1 Welcome! MSCIT 521: Knowledge Discovery and Data Mining Qiang Yang Hong Kong University of Science and Technology

2015/6/1Course Introduction2 2 Data Mining: An Example — 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

2015/6/1Course Introduction3 3 Data Mining: Convergence of Three Technologies

2015/6/1Course Introduction4 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

2015/6/1Course Introduction5 5 How are Models Built and Used? — High Level View :

2015/6/1Course Introduction6 6 The Data Mining Process

2015/6/1Course Introduction7 7 What does the Real World Look Like

2015/6/1Course Introduction8 8 Predictive Models are … Decision Trees Nearest Neighbor Classification Neural Networks Rule Induction Clustering

2015/6/1Course Introduction9 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: Broad survey of recent research  Focus 3: Hands-on, apply algorithms to KDD data sets

2015/6/1Course Introduction10 Topic 1: Foundations  Classification algorithms  Clustering algorithms  Association algorithms  Sequential Data Mining  Novel Applications  Web  Customer Relationship Management  Biological Data

2015/6/1Course Introduction11 Topic 2: Hands On  Apply learned algorithms to selected data sets  Homework assignments  Get familiar with existing system packages and libraries  In-class workshops  Programming Assignments

2015/6/1Course Introduction12 Important Sites  Instructor Web Site   TA: Kaixiang Mo  Assignment Hand-in: online   Course Discussion Site:  Check out the web cite

2015/6/1Course Introduction13 Prerequisites  Statistics and Probability would help,  but not necessary  Pattern Recognition would help,  but not necessary  Databases  Knowledge of SQL and relational algebra  But not necessary  One programming language  One of Java, C++, Perl, Matlab, etc.  Will need to read Java Library

2015/6/1Course Introduction14 Grading  Grade Distribution:  Assignments 20%  Course Project 20%  Exams 60%  Midterm 20%  Final 40%

2015/6/1Course Introduction15 More info Textbooks: For reference only Introduction to Data Mining by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, Pearson International Edition, Data Mining. by Ian Witten and Ebe Frank. (Google books)Google books Data Mining -- Concepts and Techniques by Jiawei Han and Micheline Kamber. Morgan Kaufmann Publishers. Available in our bookstore