1. 1 1 Data Mining: Concepts and Techniques (3 rd ed.) — Chapter 1 — Jiawei Han, Micheline Kamber, and Jian Pei University of Illinois at Urbana-Champaign & Simon Fraser University ©2013 Han, Kamber & Pei. All rights reserved.

2. 2 Chapter 1. Introduction What Is Data Mining ? Why 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? Major Issues in Data Mining A Brief History of Data Mining and Data Mining Society Summary

3. 3 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 in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc.

4. What Is Data Mining? If you are doing Gold Mining it means you are looking for Gold in Rocks. Data Mining suggest that we are looking for data but in fact we are looking for KNOWLEDGE in the rocks of DATA therefore.... Data mining should have been appropriately named “knowledge mining from data” or “knowledge mining”. 4

5. 5 Chapter 1. Introduction What Is Data Mining ? Why 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? Major Issues in Data Mining A Brief History of Data Mining and Data Mining Society Summary

6. 6 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, sales promotions, company profiles and performance, and customer feedback … 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

7. 7 Figure 1.2 The world is data rich but information poor.

8. 8 Figure 1.3 Data mining—searching for knowledge (interesting patterns) in data

9. Why Data Mining? Credit ratings/targeted marketing: Given a database of 100,000 names, which persons are the least likely to default on their credit cards? Identify likely responders to sales promotions Fraud detection Which types of transactions are likely to be fraudulent, given the demographics and transactional history of a particular customer? Customer relationship management: Which of my customers are likely to be the most loyal, and which are most likely to leave for a competitor? : Data Mining helps extract such information 9

10. Knowledge Discovery in Data (KDD) Knowledge Discovery in Data (KDD) is a process of which data mining forms just one part, albite a central one. 10

11. 11 Knowledge Discovery in Data (KDD) Process Data Cleaning Data Integration Databases Data Warehouse Task-relevant Data Selection Data Mining Pattern Evaluation Data mining plays an essential role in the knowledge discovery process Data mining as a step in the process of knowledge discovery.

12. 12 The knowledge discovery(KDD) process The knowledge discovery process usually involves iterative sequence of the following steps: 1. Data integration- to combine multiple source 2. Data cleaning- to remove noise and inconsistent data 3. Data selection - to retrieve relevant data for analysis 4. Data transformation - to transform data into appropriate form for data mining 5. Data mining- Implementing techniques to extract patterns of interest 6. Pattern evaluation - Where the mined data are being tested and assessed for understanding the synthesized knowledge and its range of validity 7. knowledge presentation- Presenting the results to the decision-maker

13. 13 Data Mining in Business Intelligence Increasing potential to support business decisions End User Business Analyst Data Analyst DBA Decision Making Data Presentation Visualization Techniques Data Mining Information Discovery Data Exploration Statistical Summary, Querying, and Reporting Data Preprocessing/Integration, Data Warehouses Data Sources Paper, Files, Web documents, Scientific experiments, Database Systems

14. 14 Chapter 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? Major Issues in Data Mining A Brief History of Data Mining and Data Mining Society Summary

15. 15 Data Can Be Mined Data to be mined Database data (extended-relational, object-oriented, heterogeneous, legacy), data warehouse, transactional data, stream, 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.

16. 16 Chapter 1. Introduction Why Data Mining? What Is 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? Major Issues in Data Mining A Brief History of Data Mining and Data Mining Society Summary

17. Data Mining Functionalities Data Mining functionalities are used to specify the kind of patterns to be found in data mining tasks. Data Mining tasks can be classified into two categories: 1- Descriptive tasks. 2- Predictive tasks. Descriptive Tasks can help an organization to understand its customers, but predictive Tasks is necessary to facilitate the desired outcomes. Both descriptive and predictive modeling constitute key elements of data mining and Web mining.predictive data miningWeb mining 17

18. 1- Descriptive Characterize general properties of data in the database. Objective: To derive patterns (correlation,trends ) that summarizes the underlying relationship between data. Examples: Identifying web pages that are accessed together.(human interpretable pattern). Categorize customers by their product preferences and life stage.(identify many different relationships between customers or products). customer groups are clustered according to demographics, purchasing behavior, expressed interests and other descriptive factors. 18

19. 2- Predictive perform inference on data to make predictions. Objective: Predict the value of a specific attribute (target/dependent variable)based on the value of other attributes (explanatory). The core of predictive task relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting it to predict future outcomes Example: Judge if a patient has specific disease based on his/her medical tests results. 19

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21. 21 Data Mining Function: (1) Association and Correlation Analysis Frequent patterns (or frequent item sets) A frequent item set typically refers to a set of items that often appear together in a transactional data set. What items are frequently purchased together in your Walmart? example, milk and bread, which are frequently bought together in grocery stores by many customers. Mining frequent patterns leads to the discovery of interesting associations and correlations within data. Association, correlation vs. causality A typical association rule Computer  Software [0.5%, 75%] (support, confidence) Are strongly associated items also strongly correlated?

22. 22 Data Mining Function: (2) Classification Classification and label prediction (Predictive) Is the process of construct models (or function) that describes and distinguishes data classes or concepts for future prediction. The model is derived based on the analysis of a set of training data and is used to predict the class label of objects (data) for which the class label is unknown. E.g., classify countries based on (climate), or classify cars based on (gas mileage) 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, …

23. Data Mining Function: (2) Classification 23 “ How is the derived model presented?” The derived model may be represented in various forms, such as classification rules (i.e., IF-THEN rules), decision trees, mathematical formulae, or neural networks IF-THEN Rules Decision Tree For example, a company wants to classify their prospect customers. When a new customer comes, they have to determine if this is a customer who is going to buy their products or not.

24. 24 Data Mining Function: (3) 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 Clustering can be used to generate class labels for a group of data which did not exist at the beginning. Principle: Maximizing intra-class similarity & minimizing inter- class similarity Da ta points in one cluster are more similar to one another. Data points in separate clusters are less similar to one another.

25. 25 Data Mining Function: (4) 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 Useful in fraud detection, rare events analysis

26. Are All Patterns Interesting? A data mining system has the potential to generate thousands or even millions of patterns, or rules We need to answer three questions to say if patterns are interesting 1. What makes a pattern interesting? 2. Can a data mining system generate all of the interesting patterns? 3. Can the system generate only the interesting ones? 26

27. Are All Patterns Interesting? 27 What makes a pattern is interesting? a pattern is interesting if it is easily understood by humans, Novel, Potentially useful or desired, and valid Valid on new set of data with a degree of certainty validates a hypothesis that user sought to confirm Not known before

28. Are All Patterns Interesting? Can a data mining system generate all of the interesting patterns? A data mining algorithm is complete if it mines all interesting patterns. It is often unrealistic and inefficient for data mining systems to generate all possible patterns. Instead, user- provided constraints and interestingness measures should be used to focus the search. For some mining tasks, such as association, this is often sufficient to ensure the completeness of the algorithm. 28

29. Can a data mining system generate only interesting patterns? A data mining algorithm is consistent if it mines only interesting patterns. It is an optimization problem. It is highly desirable for data mining systems to generate only interesting patterns. This would be efficient for users and data mining systems because neither would have to search through the patterns generated to identify the truly interesting ones. Sufficient progress has been made in this direction, but it still a challenging issue in data mining. 29

30. 30 Chapter 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? Major Issues in Data Mining A Brief History of Data Mining and Data Mining Society Summary

31. 31 Data Mining: Confluence of Multiple Disciplines Data Mining Machine Learning Statistics Applications Algorithm Pattern Recognition High-Performance Computing Visualization Database Technology

32. 32 Why Confluence of Multiple Disciplines? Tremendous amount of data Algorithms must be scalable to handle big data High-dimensionality of data Micro-array 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 and information networks Spatial, spatiotemporal, multimedia, text and Web data Software programs, scientific simulations New and sophisticated applications

33. 33 Chapter 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? Major Issues in Data Mining A Brief History of Data Mining and Data Mining Society Summary

34. 34 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 From major dedicated data mining systems/tools (e.g., SAS, MS SQL- Server Analysis Manager, Oracle Data Mining Tools) to invisible data mining

35. 35 Summary Data mining: Discovering interesting patterns and knowledge from massive amount of data A natural evolution of science and information 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, trend and outlier analysis, etc. Data mining technologies and applications Major issues in data mining

36. September 21, 2015 Data Mining: Concepts and Techniques 36

37. 37 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), WSDM (2008), etc. ACM Transactions on KDD (2007)

38. 38 Conferences and Journals on Data Mining KDD Conferences ACM SIGKDD Int. Conf. on Knowledge Discovery in Databases and Data Mining (KDD) SIAM Data Mining Conf. (SDM) (IEEE) Int. Conf. on Data Mining (ICDM) European Conf. on Machine Learning and Principles and practices of Knowledge Discovery and Data Mining (ECML-PKDD) Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD) Int. Conf. on Web Search and Data Mining (WSDM) Other related conferences DB conferences: ACM SIGMOD, VLDB, ICDE, EDBT, ICDT, … Web and IR conferences: WWW, SIGIR, WSDM ML conferences: ICML, NIPS PR conferences: CVPR, Journals Data Mining and Knowledge Discovery (DAMI or DMKD) IEEE Trans. On Knowledge and Data Eng. (TKDE) KDD Explorations ACM Trans. on KDD

39. 39 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. 2005