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Data Mining Samia M. Abd-Alhalem 1  Course’s Information: Course: Data Mining and Exploration (CSC213) Credits: 3 Hrs Pre- Requisites:Database Management Systems (CSC125) Department : General Instructor: Dr. Samia M. Abd-Alhalem email: [email protected]....

Data Mining Samia M. Abd-Alhalem 1  Course’s Information: Course: Data Mining and Exploration (CSC213) Credits: 3 Hrs Pre- Requisites:Database Management Systems (CSC125) Department : General Instructor: Dr. Samia M. Abd-Alhalem email: [email protected] 2  Course’s Description: - This course has been designed to give the students an introduction to data mining and hands on experience with all phases of the data mining process using real data and modern tools. - It covers many topics such as data formats, and cleaning; make predication using supervised and unsupervised learning using python and other tools, and sound evaluation methods; and data / knowledge visualization. 3 Course’s Objectives Regression. 4  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, … 5  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? ◼ How to mine such patterns and rules efficiently in large datasets? ◼ How to use such patterns for classification, clustering, and other applications? 6  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 7 Course’s Outcome  Textbook & Course Coverage Text book: “Data Mining: Concepts and Techniques, 3rd Edition” by J. Han, M. Kamber, and J. Pei – 2012. 9  Course’s Outline 1. Introduction (Chapter 1) 2. Getting to Know Your Data (Chapter 2) 3. Data Preprocessing (Chapter 3) 4. Mining Frequent Patterns & Association: Basic Concepts (Chapter 6) 5. Classification: Basic Concepts (Chapter 8) 6. Cluster Analysis: Basic Concepts and Methods(Chapter10) 7. Outlier Analysis (Chapter 12) 8. Data Mining Trends and Research Frontiers (Chapter 13) 10 Teaching Methods: - Lectures, Sections, - Discussion Groups, - Team Work - Using Presentations. 11 Evaluation Criterion “Grades”: Data Mining: Concepts and Techniques Course Requirements: 13  Agenda (Lec. 1) ✓ Introduction (Chapter 1) o Why Data Mining? o What is Data Mining? o Knowledge Discovery Process o Data Mining Tasks o What Kinds of Technologies Are Used? o Summary 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 15  Evolution of Sciences ◼ Before 1600, empirical science ◼ 1600-1950s, theoretical science ◼ Each discipline has grown a theoretical component. Theoretical models often motivate experiments and generalize our understanding. ◼ 1950s-1990s, computational science ◼ Over the last 50 years, most disciplines have grown a third, computational branch (e.g. empirical, theoretical, and computational ecology, or physics, or linguistics.) ◼ Computational Science traditionally meant simulation. It grew out of our inability to find closed-form solutions for complex mathematical models. ◼ 1990-now, data science ◼ The flood of data from new scientific instruments and simulations ◼ The ability to economically store and manage petabytes of data online ◼ The Internet and computing Grid that makes all these archives universally accessible ◼ Scientific info. management, acquisition, organization, query, and visualization tasks scale almost linearly with data volumes. Data mining is a major new challenge! ◼ Jim Gray and Alex Szalay, The World Wide Telescope: An Archetype for Online Science, Comm. ACM, 45(11): 50-54, Nov. 2002 16  Chapter 1. Introduction ◼ Why Data Mining? ◼ What Is Data Mining? ◼ Data Mining Tasks ◼ What Kind of Applications Are Targeted? ◼ Major Issues in Data Mining ◼ Summary 17  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 ◼ Data mining: a misnomer? ◼ Alternative names ◼ Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc. 18  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 19 Data Mining: Confluence of Multiple Disciplines Machine Pattern Statistics Learning Recognition Applications Data Mining Visualization Algorithm Database High-Performance Technology Computing 20 Data Mining Tasks 21  Data Mining Tasks … Data Tid Refund Marital Taxable Status Income Cheat 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No 4 Yes Married 120K No 5 No Divorced 95K Yes 6 No Married 60K No 7 Yes Divorced 220K No 8 No Single 85K Yes 9 No Married 75K No 10 No Single 90K Yes 11 No Married 60K No 12 Yes Divorced 220K No 13 No Single 85K Yes 14 No Married 75K No 15 No Single 90K Yes 10 Milk 22  Predictive Modeling: Classification l Find a model for class attribute as a function of the values of other attributes Model for predicting credit worthiness Class Employed # years at Level of Credit Yes Tid Employed present No Education Worthy address 1 Yes Graduate 5 Yes 2 Yes High School 2 No No Education 3 No Undergrad 1 No { High school, 4 Yes High School 10 Yes Graduate Undergrad } 10 … … … … … Number of Number of years years > 3 yr < 3 yr > 7 yrs < 7 yrs Yes No Yes No 23  Classification Example # years at Level of Credit Tid Employed present Education Worthy address 1 Yes Undergrad 7 ? # years at 2 No Graduate 3 ? Level of Credit Tid Employed present 3 Yes High School 2 ? Education Worthy address … … … … … 1 Yes Graduate 5 Yes 10 2 Yes High School 2 No 3 No Undergrad 1 No 4 Yes High School 10 Yes … … … … … 10 Test Set Training Learn Classifier Model Set 24 Examples of Classification Task l Classifying credit card transactions as legitimate or fraudulent l Classifying land covers (water bodies, urban areas, forests, etc.) using satellite data l Categorizing news stories as finance, weather, entertainment, sports, etc l Identifying intruders in the cyberspace l Predicting tumor cells as benign or malignant l Classifying secondary structures of protein as alpha-helix, beta-sheet, or random coil 25 Regression l Predict a value of a given continuous valued variable based on the values of other variables, assuming a linear or nonlinear model of dependency. l Extensively studied in statistics, neural network fields. l Examples: – Predicting sales amounts of new product based on advetising expenditure. – Predicting wind velocities as a function of temperature, humidity, air pressure, etc. – Time series prediction of stock market indices. 26 Clustering l Finding groups of objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups Inter-cluster Intra-cluster distances are distances are maximized minimized 27  Applications of Cluster Analysis l Understanding – Custom profiling for targeted marketing – Group related documents for browsing – Group genes and proteins that have similar functionality – Group stocks with similar price fluctuations l Summarization – Reduce the size of large data sets Courtesy: Michael Eisen Clusters for Raw SST and Raw NPP 90 Use of K-means to 60 Land Cluster 2 partition Sea Surface 30 Temperature (SST) and Land Cluster 1 Net Primary Production latitude 0 (NPP) into clusters that Ice or No NPP -30 reflect the Northern Sea Cluster 2 and Southern -60 Hemispheres. Sea Cluster 1 -90 -180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180 Cluster 28 longitude Association Rule Discovery: Definition l Given a set of records each of which contain some number of items from a given collection – Produce dependency rules which will predict occurrence of an item based on occurrences of other items. TID Items 1 Bread, Coke, Milk Rules Discovered: 2 Beer, Bread {Milk} --> {Coke} 3 Beer, Coke, Diaper, Milk {Diaper, Milk} --> {Beer} 4 Beer, Bread, Diaper, Milk 5 Coke, Diaper, Milk 29 Association Analysis: Applications l Market-basket analysis – Rules are used for sales promotion, shelf management, and inventory management l Telecommunication alarm diagnosis – Rules are used to find combination of alarms that occur together frequently in the same time period l Medical Informatics – Rules are used to find combination of patient symptoms and test results associated with certain diseases 30 Motivating Challenges l Scalability l High Dimensionality l Heterogeneous and Complex Data l Data Ownership and Distribution l Non-traditional Analysis 31  Chapter 1. Introduction ◼ Why Data Mining? ◼ What Is Data Mining? ◼ Data Mining Tasks ◼ What Kind of Applications Are Targeted? ◼ Summary 32  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 33  Chapter 1. Introduction ◼ Why Data Mining? ◼ What Is Data Mining? ◼ What Kind of Applications Are Targeted? ◼ Summary 34  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: association, classification, clustering, outlier and trend analysis, etc. ◼ Data mining technologies and applications ◼ Major issues in data mining 35  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 36