Data Mining CSC466 - Lecture 01 PDF

DATA MINING LECTURE 01 Dr. Mahmoud Mounir [email protected]  COURSE OUTLINE Data Mining ◼ Overview of Data Mining. ◼ Data Exploration o Data Preprocessing. o Outlier Analysis. ◼ Association Analysis o Mining Frequent Patterns...

DATA MINING LECTURE 01 Dr. Mahmoud Mounir [email protected]  COURSE OUTLINE Data Mining ◼ Overview of Data Mining. ◼ Data Exploration o Data Preprocessing. o Outlier Analysis. ◼ Association Analysis o Mining Frequent Patterns: Apriori Algorithm, FPGrowth, ECLAT. o Associations Rules Evaluation and Correlations. 2  COURSE OUTLINE ◼ Correlation Analysis o Probability and Statistics Basic Concepts. o Correlation and Linear Regression. ◼ Unsupervised Learning (Cluster Analysis) o Centroid-Based Methods: K-Means. o Distance-Based Methods: Hierarchical Clustering. o Density-Based Methods: DBSCAN. o Clustering Model Evaluation. 3  COURSE OUTLINE ◼ Supervised Learning (Classification) o Decision Trees. o Naïve Bayes Classification. o K-Nearest Neighbors (KNN). o Rule-based classification. o Classification Model Evaluation and Selection. ◼ Graph-Based Mining and Social Network Analysis. 4  COURSE OUTLINE A- Methods Used 1. Midterm 2. Quizzes 3. Practical 4. Lecture Tasks 5. Final Exam B- Assessment Schedule 1. Midterm week 8 2. Quizzes week5 ,week10 3. Practical week4, week9 4. Lecture Tasks During Semester 5. Final Exam week 15 C- Weighting of Assessments 1. Midterm 10% 2. Quizzes 10% 3. Practical 15% 4. Lecture Tasks 5% 5. Final Exam 60% Total 100% 5  REFERENCES ❑ Jiawei Han, Micheline Kamber and Jian Pei, “Data Mining: Concepts and Techniques”, 3rd edition, The Morgan Kaufmann Series in Data Management Systems ❑ Pang-Ning Tan, Michael Steinbach and Vipin Kumar, “Introduction to Data Mining”, 2nd Edition, Addison Wesley, 2014. 6 COURSE MATERIALS 7  Data Mining: Concepts and Techniques (3rd ed.) — Chapter 1 — Jiawei Han, Micheline Kamber, and Jian Pei University of Illinois at Urbana-Champaign & Simon Fraser University ©2011 Han, Kamber & Pei. All rights reserved. 8  Chapter 1. Introduction ◼ Why Data Mining? ◼ What Is Data Mining? ◼ A Multi-Dimensional View of Data Mining ◼ What Kind of Data Can Be Mined? ◼ What Kinds of Patterns Can Be Mined? ◼ What Technology Are Used? ◼ What Kind of Applications Are Targeted? ◼ Major Issues in Data Mining ◼ A Brief History of Data Mining and Data Mining Society ◼ Summary 9  Large-scale Data is Everywhere! ▪ There has been enormous data growth in both commercial and scientific databases due to advances in data generation Cyber Security E-Commerce and collection technologies ▪ New mantra ▪ Gather whatever data you can whenever and wherever possible. ▪ Expectations Traffic Patterns Social Networking: Twitter ▪ Gathered data will have value either for the purpose collected or for a purpose not envisioned. Sensor Networks Computational Simulations 10 Why Data Mining? Commercial Viewpoint ◼ Lots of data is being collected and warehoused ◼ Web data ◼ Google has Peta Bytes of web data ◼ Facebook has billions of active users ◼ purchases at department/ grocery stores, e-commerce ◼ Amazon handles millions of visits/day ◼ Bank/Credit Card transactions ◼ Computers have become cheaper and more powerful ◼ Competitive Pressure is Strong ◼ Provide better, customized services for an edge (e.g. in Customer Relationship Management) 11  Why Data Mining? Scientific Viewpoint ◼ Data collected and stored at enormous speeds ◼ remote sensors on a satellite ◼ NASA EOSDIS archives over fMRI Data from Brain Sky Survey Data petabytes of earth science data / year ◼ telescopes scanning the skies ◼ Sky survey data ◼ High-throughput biological data ◼ scientific simulations ◼ terabytes of data generated in a few hours Gene Expression Data ◼ Data mining helps scientists ◼ in automated analysis of massive datasets ◼ In hypothesis formation Surface Temperature of Earth 12  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 13  Evolution of Database Technology ◼ 1960s: ◼ Data collection, database creation, Information Management System (IMS) and network DBMS ◼ 1970s: ◼ Relational data model, relational DBMS implementation ◼ 1980s: ◼ RDBMS, advanced data models (extended-relational, OO, deductive, etc.) ◼ Application-oriented DBMS (spatial, scientific, engineering, etc.) ◼ 1990s: ◼ Data mining, data warehousing, multimedia databases, and Web databases ◼ 2000s ◼ Stream data management and mining ◼ Data mining and its applications ◼ Web technology (XML, data integration) and global information systems 14  Chapter 1. Introduction ◼ Why Data Mining? ◼ What Is Data Mining? ◼ A Multi-Dimensional View of Data Mining ◼ What Kind of Data Can Be Mined? ◼ What Kinds of Patterns Can Be Mined? ◼ What Technology Are Used? ◼ What Kind of Applications Are Targeted? ◼ Major Issues in Data Mining ◼ A Brief History of Data Mining and Data Mining Society ◼ Summary 15  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 ◼ Exploration & analysis, by automatic or semi-automatic means, of large quantities of data in order to discover meaningful patterns ◼ Alternative names ◼ Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc. ◼ Watch out: Is everything “data mining”? ◼ Simple search and query processing ◼ (Deductive) expert systems 16  Knowledge Discovery (KDD) Process ◼ This is a view from typical database systems and data Pattern Evaluation warehousing communities ◼ Data mining plays an essential role in the knowledge discovery process Data Mining Task-relevant Data Data Warehouse Selection Data Cleaning Data Integration Databases 17  Example: A Web Mining Framework ◼ Web mining usually involves ◼ Data cleaning ◼ Data integration from multiple sources ◼ Warehousing the data ◼ Data cube construction ◼ Data selection for data mining ◼ Data mining ◼ Presentation of the mining results ◼ Patterns and knowledge to be used or stored into knowledge-base 18  Data Mining in Business Intelligence Increasing potential to support business decisions End User Decision Making Data Presentation Business Analyst Visualization Techniques Data Mining Data Information Discovery Analyst Data Exploration Statistical Summary, Querying, and Reporting Data Preprocessing/Integration, Data Warehouses DBA Data Sources Paper, Files, Web documents, Scientific experiments, Database Systems 19 Example: Mining vs. Data Exploration ◼ Business intelligence view ◼ Warehouse, data cube, reporting but not much mining ◼ Business objects vs. data mining tools ◼ Supply chain example: tools ◼ Data presentation ◼ Exploration 20  KDD Process: A Typical View from ML and Statistics Input Data Data Pre- Data Post- Processing Mining Processing Data integration Pattern discovery Pattern evaluation Normalization Association & correlation Pattern selection Feature selection Classification Pattern interpretation Clustering Dimension reduction Pattern visualization Outlier analysis ………… ◼ This is a view from typical machine learning and statistics communities 21  Example: Medical Data Mining ◼ Health care & medical data mining – often adopted such a view in statistics and machine learning ◼ Preprocessing of the data (including feature extraction and dimension reduction) ◼ Classification or/and clustering processes ◼ Post-processing for presentation 22  Chapter 1. Introduction ◼ Why Data Mining? ◼ What Is Data Mining? ◼ A Multi-Dimensional View of Data Mining ◼ What Kind of Data Can Be Mined? ◼ What Kinds of Patterns Can Be Mined? ◼ What Technology Are Used? ◼ What Kind of Applications Are Targeted? ◼ Major Issues in Data Mining ◼ A Brief History of Data Mining and Data Mining Society ◼ Summary 23  Multi-Dimensional View of Data Mining ◼ Data to be mined ◼ 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. 24  Chapter 1. Introduction ◼ Why Data Mining? ◼ What Is Data Mining? ◼ A Multi-Dimensional View of Data Mining ◼ What Kind of Data Can Be Mined? ◼ What Kinds of Patterns Can Be Mined? ◼ What Technology Are Used? ◼ What Kind of Applications Are Targeted? ◼ Major Issues in Data Mining ◼ A Brief History of Data Mining and Data Mining Society ◼ Summary 25  Data Mining: On What Kinds of Data? ◼ Database-oriented data sets and applications ◼ Relational database, data warehouse, transactional database ◼ Advanced data sets and advanced applications ◼ Data streams and sensor data ◼ Time-series data, temporal data, sequence data (incl. bio-sequences) ◼ Structure data, graphs, social networks and multi-linked data ◼ Object-relational databases ◼ Heterogeneous databases and legacy databases ◼ Spatial data and spatiotemporal data ◼ Multimedia database ◼ Text databases ◼ The World-Wide Web 26  Chapter 1. Introduction ◼ Why Data Mining? ◼ What Is Data Mining? ◼ A Multi-Dimensional View of Data Mining ◼ What Kind of Data Can Be Mined? ◼ What Kinds of Patterns Can Be Mined? ◼ What Technology Are Used? ◼ What Kind of Applications Are Targeted? ◼ Major Issues in Data Mining ◼ A Brief History of Data Mining and Data Mining Society ◼ Summary 27 Data Mining Function: (1) Generalization ◼ Information integration and data warehouse construction ◼ Data cleaning, transformation, integration, and multidimensional data model ◼ Data cube technology ◼ Scalable methods for computing (i.e., materializing) multidimensional aggregates ◼ OLAP (online analytical processing) ◼ Multidimensional concept description: Characterization and discrimination ◼ Generalize, summarize, and contrast data characteristics, e.g., dry vs. wet region 28 Data Mining Function: (2) 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? ◼ How to mine such patterns and rules efficiently in large datasets? ◼ How to use such patterns for classification, clustering, and other applications? 29 Data Mining Function: (3) 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 Data Mining Function: (4) 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 Data Mining Function: (5) Outlier Analysis ◼ Outlier analysis ◼ Outlier: A data object that does not comply with the general behavior of the data ◼ Noise or exception? ― One person’s garbage could be another person’s treasure ◼ Methods: by product of clustering or regression analysis, … ◼ Useful in fraud detection, rare events analysis 32 Time and Ordering: Sequential Pattern, Trend and Evolution Analysis ◼ Sequence, trend and evolution analysis ◼ Trend, time-series, and deviation analysis: e.g., regression and value prediction ◼ Sequential pattern mining ◼ e.g., first buy digital camera, then buy large SD memory cards ◼ Periodicity analysis ◼ Motifs and biological sequence analysis ◼ Approximate and consecutive motifs ◼ Similarity-based analysis ◼ Mining data streams ◼ Ordered, time-varying, potentially infinite, data streams 33  Structure and Network Analysis ◼ Graph mining ◼ Finding frequent subgraphs (e.g., chemical compounds), trees (XML), substructures (web fragments) ◼ Information network analysis ◼ Social networks: actors (objects, nodes) and relationships (edges) ◼ e.g., author networks in CS, terrorist networks ◼ Multiple heterogeneous networks ◼ A person could be multiple information networks: friends, family, classmates, … ◼ Links carry a lot of semantic information: Link mining ◼ Web mining ◼ Web is a big information network: from PageRank to Google ◼ Analysis of Web information networks ◼ Web community discovery, opinion mining, usage mining, … 34  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 ◼ … 35  Chapter 1. Introduction ◼ Why Data Mining? ◼ What Is Data Mining? ◼ A Multi-Dimensional View of Data Mining ◼ What Kind of Data Can Be Mined? ◼ What Kinds of Patterns Can Be Mined? ◼ What Technology Are Used? ◼ What Kind of Applications Are Targeted? ◼ Major Issues in Data Mining ◼ A Brief History of Data Mining and Data Mining Society ◼ Summary 36 Data Mining: Confluence of Multiple Disciplines Machine Pattern Statistics Learning Recognition Applications Data Mining Visualization Algorithm Database High-Performance Technology Computing 37 Why Confluence of Multiple Disciplines? ◼ Tremendous amount of data ◼ Algorithms must be highly scalable to handle such as tera-bytes of data ◼ High-dimensionality of data ◼ Microarray may have tens of thousands of dimensions ◼ High complexity of data ◼ Data streams and sensor data ◼ Time-series data, temporal data, sequence data ◼ Structure data, graphs, social networks and multi-linked data ◼ Heterogeneous databases and legacy databases ◼ Spatial, spatiotemporal, multimedia, text and Web data ◼ Software programs, scientific simulations ◼ New and sophisticated applications 38  Chapter 1. Introduction ◼ Why Data Mining? ◼ What Is Data Mining? ◼ A Multi-Dimensional View of Data Mining ◼ What Kind of Data Can Be Mined? ◼ What Kinds of Patterns Can Be Mined? ◼ What Technology Are Used? ◼ What Kind of Applications Are Targeted? ◼ Major Issues in Data Mining ◼ A Brief History of Data Mining and Data Mining Society ◼ Summary 39  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. 2009 issue) ◼ From major dedicated data mining systems/tools (e.g., SAS, MS SQL- Server Analysis Manager, Oracle Data Mining Tools) to invisible data mining 40  Chapter 1. Introduction ◼ Why Data Mining? ◼ What Is Data Mining? ◼ A Multi-Dimensional View of Data Mining ◼ What Kind of Data Can Be Mined? ◼ What Kinds of Patterns Can Be Mined? ◼ What Technology Are Used? ◼ What Kind of Applications Are Targeted? ◼ Major Issues in Data Mining ◼ A Brief History of Data Mining and Data Mining Society ◼ Summary 41  Major Issues in Data Mining (1) ◼ Mining Methodology ◼ Mining various and new kinds of knowledge ◼ Mining knowledge in multi-dimensional space ◼ Data mining: An interdisciplinary effort ◼ Boosting the power of discovery in a networked environment ◼ Handling noise, uncertainty, and incompleteness of data ◼ Pattern evaluation and pattern- or constraint-guided mining ◼ User Interaction ◼ Interactive mining ◼ Incorporation of background knowledge ◼ Presentation and visualization of data mining results 42  Major Issues in Data Mining (2) ◼ Efficiency and Scalability ◼ Efficiency and scalability of data mining algorithms ◼ Parallel, distributed, stream, and incremental mining methods ◼ Diversity of data types ◼ Handling complex types of data ◼ Mining dynamic, networked, and global data repositories ◼ Data mining and society ◼ Social impacts of data mining ◼ Privacy-preserving data mining ◼ Invisible data mining 43  Chapter 1. Introduction ◼ Why Data Mining? ◼ What Is Data Mining? ◼ A Multi-Dimensional View of Data Mining ◼ What Kind of Data Can Be Mined? ◼ What Kinds of Patterns Can Be Mined? ◼ What Technology Are Used? ◼ What Kind of Applications Are Targeted? ◼ Major Issues in Data Mining ◼ A Brief History of Data Mining and Data Mining Society ◼ Summary 44  A Brief History of Data Mining Society ◼ 1989 IJCAI Workshop on Knowledge Discovery in Databases ◼ Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley, 1991) ◼ 1991-1994 Workshops on Knowledge Discovery in Databases ◼ Advances in Knowledge Discovery and Data Mining (U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, 1996) ◼ 1995-1998 International Conferences on Knowledge Discovery in Databases and Data Mining (KDD’95-98) ◼ Journal of Data Mining and Knowledge Discovery (1997) ◼ ACM SIGKDD conferences since 1998 and SIGKDD Explorations ◼ More conferences on data mining ◼ PAKDD (1997), PKDD (1997), SIAM-Data Mining (2001), (IEEE) ICDM (2001), etc. ◼ ACM Transactions on KDD starting in 2007 45  Conferences and Journals on Data Mining ◼ KDD Conferences ◼ Other related conferences ◼ ACM SIGKDD Int. Conf. on ◼ DB conferences: ACM SIGMOD, Knowledge Discovery in VLDB, ICDE, EDBT, ICDT, … Databases and Data Mining (KDD) ◼ Web and IR conferences: WWW, ◼ SIAM Data Mining Conf. (SDM) SIGIR, WSDM ◼ (IEEE) Int. Conf. on Data Mining (ICDM) ◼ ML conferences: ICML, NIPS ◼ European Conf. on Machine ◼ PR conferences: CVPR, Learning and Principles and ◼ Journals practices of Knowledge Discovery ◼ Data Mining and Knowledge and Data Mining (ECML-PKDD) Discovery (DAMI or DMKD) ◼ Pacific-Asia Conf. on Knowledge Discovery and Data Mining ◼ IEEE Trans. On Knowledge and (PAKDD) Data Eng. (TKDE) ◼ Int. Conf. on Web Search and ◼ KDD Explorations Data Mining (WSDM) ◼ ACM Trans. on KDD 46 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. 47  Chapter 1. Introduction ◼ Why Data Mining? ◼ What Is Data Mining? ◼ A Multi-Dimensional View of Data Mining ◼ What Kind of Data Can Be Mined? ◼ What Kinds of Patterns Can Be Mined? ◼ What Technology Are Used? ◼ What Kind of Applications Are Targeted? ◼ Major Issues in Data Mining ◼ A Brief History of Data Mining and Data Mining Society ◼ Summary 48  Summary ◼ Data mining: Discovering interesting patterns and knowledge from massive amount of data ◼ A natural evolution of database technology, in great demand, with wide applications ◼ A KDD process includes data cleaning, data integration, data selection, transformation, data mining, pattern evaluation, and knowledge presentation ◼ Mining can be performed in a variety of data ◼ Data mining functionalities: characterization, discrimination, association, classification, clustering, outlier and trend analysis, etc. ◼ Data mining technologies and applications ◼ Major issues in data mining 49  Recommended Reference Books ◼ 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 and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 3rd ed., 2011 ◼ D. J. Hand, H. Mannila, and P. Smyth, Principles of Data Mining, MIT Press, 2001 ◼ T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed., Springer-Verlag, 2009 ◼ B. Liu, Web Data Mining, Springer 2006. ◼ T. M. Mitchell, Machine Learning, McGraw Hill, 1997 ◼ G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases. AAAI/MIT Press, 1991 ◼ 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, 2nd ed. 2005 50