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isd314-01
This document contains an introduction to a course on data mining techniques. It provides an overview of course administration including class times and assessment components. It also lists some key references and resources for the course, including data mining software and textbooks. The course will cover data mining concepts and applications over 12 weeks through lectures and hands-on exercises. Student assessment will include quizzes, assignments, a midterm exam and final exam.
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isd314-01
- 1. Teknik Data Mining Pendahuluan Anung B. Ariwibowo Teknik Data Mining - ISD314
- 2. Administratif Perkuliahan 12 Perkuliahan dalam 12 pekan Senin, 10.00 – 12.30 WIB Kehadiran Paling lambat 15 menit (Mahasiswa, Dosen) 70% minimum (Mahasiswa) 2 Anung Ariwibowo 2011-2012-3
- 3. Administratif Perkuliahan Komponen Penilaian 10% Kuis (9 + Pre-test) 20% Tugas (4) 30% UTS (Open book) 40% UAS (Open book) 5% Bonus (Aktifitas, pengumpulan dini, kreatifitas) 3 Anung Ariwibowo 2011-2012-3
- 4. Administratif Perkuliahan WEKA http://www.cs.waikato.ac.nz/ml/weka/ http://weka.wikispaces.com/Primer Toby Segaran, "Programming Collective Intelligence: Building Smart Web 2.0 Applications", O'Reilly, 1st Edition, 2007. Ian H. Witten, Eibe Frank, dan Mark A. Hall, "Data Mining: Practical Machine Learning Tools and Techniques", 3rd Edition, Morgan Kauffman, 2011. Slide buku Ian Witten, http://www.cs.waikato.ac.nz/ml/weka/book.html 4 Anung Ariwibowo 2011-2012-3
- 5. Administratif Perkuliahan Jiawei Han, Micheline Kamber, dan Jian Pei, "Data Mining: Concepts and Techniques", 3rd Edition, Morgan Kauffman, 2011. Slide buku Jiawei Han, http://www.cs.uiuc.edu/~hanj/dmbook KDNuggets, http://www.kdnuggets.com/ Slide dari Andrew Moore, http://www.autonlab.org/tutorials/list.html Slides tjerdastangkas.blogspot.com/search/label/isd314 Slide ke-0.5 dari Anung B. Ariwibowo Teknik Data Mining - NumPages ISD314
- 6. Disclaimer Following slides are derived from Jiawei Han's slide Slide ke-0.6 Anung B. Ariwibowo Teknik Data Mining - dari ISD314
- 7. Chapter 1. Introduction Motivation: Why data mining? What is data mining? Data Mining: On what kind of data? Data mining functionality Are all the patterns interesting? Classification of data mining systems Major issues in data mining 7 Data Mining: Concepts and Techniques March 12, 2012
- 8. Necessity Is theMother of Invention Data explosion problem Automated data collection tools and mature database technology lead to tremendous amounts of data accumulated and/or to be analyzed in databases, data warehouses, and other information repositories We are drowning in data, but starving for knowledge! Solution: Data warehousing and data mining Data warehousing and on-line analytical processing Miing interesting knowledge (rules, regularities, patterns, constraints) from data in large databases 8 Data Mining: Concepts and Techniques March 12, 2012
- 9. Evolution of DatabaseTechnology 1960s: Data collection, database creation, 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 with a variety of applications Web technology and global information systems 9 Data Mining: Concepts and Techniques March 12, 2012
- 10. Trends leading toData Flood More data is generated: Bank, telecom, other business transactions ... Scientific Data: astronomy, biology, etc Web, text, and e-commerce 10
- 11. Data Growth Large DB examples as of 2003: France Telecom has largest decision-support DB, ~30TB; AT&T ~ 26 TB Alexa internet archive: 7 years of data, 500 TB Google searches 3.3 Billion pages, ? TB Twice as much information was created in 2002 as in 1999 (~30% growth rate) Knowledge Discovery is NEEDED to make sense and use of data. 11
- 12. What Is DataMining? 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. Watch out: Is everything “data mining”? (Deductive) query processing. Expert systems or small ML/statistical programs 12 Data Mining: Concepts and Techniques March 12, 2012
- 13. Why Data Mining?—PotentialApplications Data analysis and decision support Market analysis and management Target marketing, customer relationship management (CRM), market basket analysis, cross selling, market segmentation Risk analysis and management Forecasting, customer retention, improved underwriting, quality control, competitive analysis Fraud detection and detection of unusual patterns (outliers) Other Applications Text mining (news group, email, documents) and Web mining Stream data mining DNA and bio-data analysis 13 Data Mining: Concepts and Techniques March 12, 2012
- 14. Market Analysis andManagement Where does the data come from? Credit card transactions, loyalty cards, discount coupons, customer complaint calls, plus (public) lifestyle studies Target marketing Find clusters of “model” customers who share the same characteristics: interest, income level, spending habits, etc. Determine customer purchasing patterns over time Cross-market analysis Associations/co-relations between product sales, & prediction based on such association Customer profiling What types of customers buy what products (clustering or classification) Customer requirement analysis identifying the best products for different customers predict what factors will attract new customers Provision of summary information multidimensional summary reports statistical summary information (data central tendency and variation) 14 Data Mining: Concepts and Techniques March 12, 2012
- 15. Corporate Analysis &Risk Management Finance planning and asset evaluation cash flow analysis and prediction contingent claim analysis to evaluate assets cross-sectional and time series analysis (financial-ratio, trend analysis, etc.) Resource planning summarize and compare the resources and spending Competition monitor competitors and market directions group customers into classes and a class-based pricing procedure set pricing strategy in a highly competitive market 15 Data Mining: Concepts and Techniques March 12, 2012
- 16. Fraud Detection &Mining Unusual Patterns Approaches: Clustering & model construction for frauds, outlier analysis Applications: Health care, retail, credit card service, telecomm. Auto insurance: ring of collisions Money laundering: suspicious monetary transactions Medical insurance Professional patients, ring of doctors, and ring of references Unnecessary or correlated screening tests Telecommunications: phone-call fraud Phone call model: destination of the call, duration, time of day or week. Analyze patterns that deviate from an expected norm Retail industry Analysts estimate that 38% of retail shrink is due to dishonest employees Anti-terrorism 16 Data Mining: Concepts and Techniques March 12, 2012
- 17. Other Applications Sports IBM Advanced Scout analyzed NBA game statistics (shots blocked, assists, and fouls) to gain competitive advantage for New York Knicks and Miami Heat Astronomy JPL and the Palomar Observatory discovered 22 quasars with the help of data mining Internet Web Surf-Aid IBM Surf-Aid applies data mining algorithms to Web access logs for market-related pages to discover customer preference and behavior pages, analyzing effectiveness of Web marketing, improving Web site organization, etc. 17 Data Mining: Concepts and Techniques March 12, 2012
- 18. Data Mining: AKDD Process Data mining—core of Pattern Evaluation knowledge discovery process Data Mining Task-relevant Data Data Warehouse Selection Data Cleaning Data Integration 18 Databases Data Mining: Concepts and Techniques March 12, 2012
- 19. Steps of aKDD Process Learning the application domain relevant prior knowledge and goals of application Creating a target data set: data selection Data cleaning and preprocessing: (may take 60% of effort!) Data reduction and transformation Find useful features, dimensionality/variable reduction, invariant representation. Choosing functions of data mining summarization, classification, regression, association, clustering. Choosing the mining algorithm(s) Data mining: search for patterns of interest Pattern evaluation and knowledge presentation visualization, transformation, removing redundant patterns, etc. Use of discovered knowledge 19 Data Mining: Concepts and Techniques March 12, 2012
- 20. Data Mining andBusiness Intelligence Increasing potential to support business decisions End User Making Decisions Data Presentation Business Analyst Visualization Techniques Data Mining Data Information Discovery Analyst Data Exploration Statistical Analysis, Querying and Reporting Data Warehouses / Data Marts OLAP, MDA DBA Data Sources Paper, Files, Information Providers, Database Systems, OLTP 20 Data Mining: Concepts and Techniques March 12, 2012
- 21. Data Mining: Confluenceof Multiple Disciplines Database Statistics Systems Machine Data Mining Visualization Learning Algorithm Other Disciplines 21 Data Mining: Concepts and Techniques March 12, 2012
- 22. Weather Data: Playor not Play? Outlook Temperature Humidity Windy Play? sunny hot high false No Note: sunny hot high true No Outlook is the overcast hot high false Yes Forecast, rain mild high false Yes no relation to rain cool normal false Yes Microsoft rain cool normal true No email program overcast cool normal true Yes sunny mild high false No sunny cool normal false Yes rain mild normal false Yes sunny mild normal true Yes overcast mild high true Yes overcast hot normal false Yes rain mild high true No 22
- 23. Example Tree for“Play?” For Outlook=sunny, Humidity=normal, Outlook Windy=true Play = ? sunny rain overcast Humidity Yes Windy high normal true false No Yes No Yes 23
- 24. Pustaka Slides tjerdastangkas.blogspot.com/search/label/isd314 Slide ke-0.24 dari Anung B. Ariwibowo Teknik Data Mining - NumPages ISD314