CS6220: DATA MINING TECHNIQUES
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1 CS6220: DATA MINING TECHNIQUES 1: Introduction Instructor: Yizhou Sun September 8, 2014
2 Course Information Course homepage: Fall_CS6220/index.htm Class schedule Slides Announcement Assignments 2
3 Prerequisites CS 5800 or CS 7800, or consent of instructor More generally You are expected to have background knowledge in data structures, algorithms, basic linear algebra, and basic statistics. You will also need to be familiar with at least one programming language, and have programming experiences. 3
4 When Meeting Time and Location Monday, 6-9pm Where Shillman Hall 335 4
5 Instructor and TA Information Instructor: Yizhou Sun Homepage: Office: 320 WVH Office hour: Wednesdays 1-3pm TA: Yupeng Gu Office hours: Tuesdays 2:30-4:30pm at 472 WVH Kosha Shah Office hours: Thursdays 10:00am-12:00pm at 102 Main Lab WVH 5
6 Homework: 40% Midterm exam: 25% Course project: 30% Participation: 5% Grading 6
7 Homework: 40% Grading: Homework Four assignments are expected Deadline: 11:59pm of the indicated due date via Blackboard or class system No Late Submission! No copying or sharing of homework! But you can discuss general challenges and ideas with others 7
8 Grading: Midterm Exam Midterm exam: 25% Closed book exam, but you can take a cheating sheet of A4 size 8
9 Grading: Course Project Course project: 30% Group project (3-4 people for one group) Goal: Compete on the assigned course project You are expected to submit a project report and your code at the end of the semester 9
10 Participation (5%) Grading: Participation In-class participation quizzes Online participation (piazza) piazza.com/northeastern/fall2014/cs
11 Textbook Jiawei Han, Micheline Kamber, and Jian Pei. Data Mining: Concepts and Techniques, 3rd edition, Morgan Kaufmann, 2011 References "Data Mining" by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar ( "Machine Learning" by Tom Mitchell ( "Introduction to Machine Learning" by Ethem ALPAYDIN ( "Pattern Classification" by Richard O. Duda, Peter E. Hart, David G. Stork ( html) "The Elements of Statistical Learning: Data Mining, Inference, and Prediction" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman ( "Pattern Recognition and Machine Learning" by Christopher M. Bishop ( 11
12 Goal of the Course Know what is data mining and the basic algorithms Know how to apply algorithms to real-world applications Provide a starting course for research in data mining 12
13 1. Introduction Why Data Mining? What Is Data Mining? A Multi-Dimensional View of Data Mining What Kinds of Data Can Be Mined? What Kinds of Patterns Can Be Mined? What Kinds of Technologies Are Used? What Kinds of Applications Are Targeted? Content covered by this course 13
14 Why Data Mining? The Explosive Growth of Data: from terabytes to petabytes Data collection and data availability Automated data collection tools, database systems, Web, computerized society Major sources of abundant data Business: Web, e-commerce, transactions, stocks, Science: Remote sensing, bioinformatics, scientific simulation, Society and everyone: news, digital cameras, YouTube We are drowning in data, but starving for knowledge! Necessity is the mother of invention Data mining Automated analysis of massive data sets 14
15 1. Introduction Why Data Mining? What Is Data Mining? A Multi-Dimensional View of Data Mining What Kinds of Data Can Be Mined? What Kinds of Patterns Can Be Mined? What Kinds of Technologies Are Used? What Kinds of Applications Are Targeted? Content covered by this course 15
16 What Is Data Mining? Data mining (knowledge discovery from data) Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data Alternative names Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc. 16
17 Knowledge Discovery (KDD) Process This is a view from typical database systems and data warehousing communities Data mining plays an essential role in the knowledge discovery process Data Mining Pattern Evaluation Task-relevant Data Data Warehouse Selection Data Cleaning Data Integration Databases 17
18 Data Mining in Business Intelligence Increasing potential to support business decisions Decision Making Data Presentation Visualization Techniques Data Mining Information Discovery End User Business Analyst Data Analyst Data Exploration Statistical Summary, Querying, and Reporting Data Preprocessing/Integration, Data Warehouses Data Sources Paper, Files, Web documents, Scientific experiments, Database Systems DBA 18
19 KDD Process: A Typical View from ML and Statistics Input Data Data Pre- Processing Data Mining Post- Processing Data integration Normalization Feature selection Dimension reduction Pattern discovery Association & correlation Classification Clustering Outlier analysis Pattern evaluation Pattern selection Pattern interpretation Pattern visualization This is a view from typical machine learning and statistics communities 19
20 1. Introduction Why Data Mining? What Is Data Mining? A Multi-Dimensional View of Data Mining What Kinds of Data Can Be Mined? What Kinds of Patterns Can Be Mined? What Kinds of Technologies Are Used? What Kinds of Applications Are Targeted? Content covered by this course 20
21 Data to be mined Multi-Dimensional View of Data Mining Database data (extended-relational, object-oriented, heterogeneous, legacy), data warehouse, transactional data, stream, spatiotemporal, time-series, sequence, text and web, multi-media, graphs & social and information networks Knowledge to be mined (or: Data mining functions) Characterization, discrimination, association, classification, clustering, trend/deviation, outlier analysis, etc. Descriptive vs. predictive data mining Multiple/integrated functions and mining at multiple levels Techniques utilized Data-intensive, data warehouse (OLAP), machine learning, statistics, pattern recognition, visualization, high-performance, etc. Applications adapted Retail, telecommunication, banking, fraud analysis, bio-data mining, stock market analysis, text mining, Web mining, etc. 21
22 1. Introduction Why Data Mining? What Is Data Mining? A Multi-Dimensional View of Data Mining What Kinds of Data Can Be Mined? What Kinds of Patterns Can Be Mined? What Kinds of Technologies Are Used? What Kinds of Applications Are Targeted? Content covered by this course 22
23 Matrix Data 23
24 Set Data TID Items 1 Bread, Coke, Milk 2 Beer, Bread 3 Beer, Coke, Diaper, Milk 4 Beer, Bread, Diaper, Milk 5 Coke, Diaper, Milk 24
25 Sequence Data 25
26 Time Series 26
27 Graph / Network 27
28 1. Introduction Why Data Mining? What Is Data Mining? A Multi-Dimensional View of Data Mining What Kinds of Data Can Be Mined? What Kinds of Patterns Can Be Mined? What Kinds of Technologies Are Used? What Kinds of Applications Are Targeted? Content covered by this course 28
29 Data Mining Function: Association and Correlation Analysis Frequent patterns (or frequent itemsets) What items are frequently purchased together in your Walmart? Association, correlation vs. causality A typical association rule Diaper Beer [0.5%, 75%] (support, confidence) Are strongly associated items also strongly correlated? 29
30 Data Mining Function: Classification Classification and label prediction Construct models (functions) based on some training examples Describe and distinguish classes or concepts for future prediction E.g., classify countries based on (climate), or classify cars based on (gas mileage) Predict some unknown class labels Typical methods Decision trees, naïve Bayesian classification, support vector machines, neural networks, rule-based classification, pattern-based classification, logistic regression, Typical applications: Credit card fraud detection, direct marketing, classifying stars, diseases, web-pages, 30
31 Data Mining Function: Cluster Analysis Unsupervised learning (i.e., Class label is unknown) Group data to form new categories (i.e., clusters), e.g., cluster houses to find distribution patterns Principle: Maximizing intra-class similarity & minimizing interclass similarity Many methods and applications 31
32 Data Mining Functions: Others Prediction Similarity search Ranking Outlier detection 32
33 Evaluation of Knowledge Are all mined knowledge interesting? One can mine tremendous amount of patterns and knowledge Some may fit only certain dimension space (time, location, ) Some may not be representative, may be transient, Evaluation of mined knowledge directly mine only interesting knowledge? Descriptive vs. predictive Coverage Typicality vs. novelty Accuracy Timeliness 33
34 1. Introduction Why Data Mining? What Is Data Mining? A Multi-Dimensional View of Data Mining What Kinds of Data Can Be Mined? What Kinds of Patterns Can Be Mined? What Kinds of Technologies Are Used? What Kinds of Applications Are Targeted? Content covered by this course 34
35 Data Mining: Confluence of Multiple Disciplines Machine Learning Pattern Recognition Statistics Applications Data Mining Visualization Algorithm Database Technology High-Performance Computing 35
36 1. Introduction Why Data Mining? What Is Data Mining? A Multi-Dimensional View of Data Mining What Kinds of Data Can Be Mined? What Kinds of Patterns Can Be Mined? What Kinds of Technologies Are Used? What Kinds of Applications Are Targeted? Content covered by this course 36
37 Applications of Data Mining Web page analysis: from web page classification, clustering to PageRank & HITS algorithms Collaborative analysis & recommender systems Basket data analysis to targeted marketing Biological and medical data analysis: classification, cluster analysis (microarray data analysis), biological sequence analysis, biological network analysis Data mining and software engineering (e.g., IEEE Computer, Aug issue) Social media Game 37
38 1. Introduction Why Data Mining? What Is Data Mining? A Multi-Dimensional View of Data Mining What Kinds of Data Can Be Mined? What Kinds of Patterns Can Be Mined? What Kinds of Technologies Are Used? What Kinds of Applications Are Targeted? Content covered by this course 38
39 By data types: matrix data set data sequence data time series graph and network By functions: Classification Clustering Frequent pattern mining Prediction Similarity search Ranking Course Content 39
40 Methods to Learn Matrix Data Set Data Sequence Data Time Series Graph & Network Classification Decision Tree; Naïve Bayes; Logistic Regression SVM; knn HMM Label Propagation Clustering Frequent Pattern Mining K-means; hierarchical clustering; DBSCAN; Mixture Models; kernel k-means Apriori; FP-growth GSP; PrefixSpan Prediction Linear Regression Autoregression SCAN; Spectral Clustering Similarity Search Ranking DTW P-PageRank PageRank 40
41 Evaluation How to determine whether a method is good or not? Effectiveness Efficiency 41
42 Where to Find References? DBLP, CiteSeer, Google Data mining and KDD (SIGKDD: CDROM) Conferences: ACM-SIGKDD, IEEE-ICDM, SIAM-DM, PKDD, PAKDD, etc. Journal: Data Mining and Knowledge Discovery, KDD Explorations, ACM TKDD Database systems (SIGMOD: ACM SIGMOD Anthology CD ROM) Conferences: ACM-SIGMOD, ACM-PODS, VLDB, IEEE-ICDE, EDBT, ICDT, DASFAA Journals: IEEE-TKDE, ACM-TODS/TOIS, JIIS, J. ACM, VLDB J., Info. Sys., etc. AI & Machine Learning Conferences: Machine learning (ML), AAAI, IJCAI, COLT (Learning Theory), CVPR, NIPS, etc. Journals: Machine Learning, Artificial Intelligence, Knowledge and Information Systems, IEEE- PAMI, etc. Web and IR Conferences: SIGIR, WWW, CIKM, etc. Journals: WWW: Internet and Web Information Systems, Statistics Conferences: Joint Stat. Meeting, etc. Journals: Annals of statistics, etc. Visualization Conference proceedings: CHI, ACM-SIGGraph, etc. Journals: IEEE Trans. visualization and computer graphics, etc. 42
43 Recommended Reference Books E. Alpaydin. Introduction to Machine Learning, 2nd ed., MIT Press, 2011 S. Chakrabarti. Mining the Web: Statistical Analysis of Hypertex and Semi-Structured Data. Morgan Kaufmann, 2002 R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2ed., Wiley-Interscience, 2000 T. Dasu and T. Johnson. Exploratory Data Mining and Data Cleaning. John Wiley & Sons, 2003 U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, 1996 U. Fayyad, G. Grinstein, and A. Wierse, Information Visualization in Data Mining and Knowledge Discovery, Morgan Kaufmann, 2001 J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques. Morgan Kaufmann, 3 rd ed., 2011 T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2 nd ed., Springer, 2009 B. Liu, Web Data Mining, Springer 2006 T. M. Mitchell, Machine Learning, McGraw Hill, 1997 Y. Sun and J. Han, Mining Heterogeneous Information Networks, Morgan & Claypool, 2012 P.-N. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining, Wiley, 2005 S. M. Weiss and N. Indurkhya, Predictive Data Mining, Morgan Kaufmann, 1998 I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, 2 nd ed
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