An Introduction to Data Mining


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An Introduction to Data Mining

  1. 1. Computing the Future of Data Mining An Introduction to Data Mining Visit to Messiah College September 4, 2006 William M. Pottenger, Ph.D. Computer Science & Engineering Department  William M. Pottenger, Ph.D.
  2. 2. Knowledge Workers are Overwhelmed • The user of software tools and computers are domain experts, NOT the computer science professionals – Too much data – Too much technology – Not enough useful information  William M. Pottenger, Ph.D.
  3. 3. Data Mining Roots: A Confluence of Multiple Disciplines • Database Systems, Data Warehouses, and OLAP • Machine Learning • Information Theory & Statistics • Mathematical Programming • Visualization • High Performance Computing • … • Algorithms have been known for awhile…Google™  William M. Pottenger, Ph.D.
  4. 4. Data Mining: On What Kind of Data? • Relational Databases • Data Warehouses • Transactional Databases • Advanced Database Systems – Object-Relational – Text – Heterogeneous: Legacy, Distributed, … – WWW • … the Bible!   William M. Pottenger, Ph.D.
  5. 5. Why Do We Need Data Mining? • Leverage organization‟s data assets – Only a small portion (typically - 5%-10%) of the collected data is ever analyzed – Data that may never be analyzed continues to be collected, at a great expense, out of concern that something which may prove important in the future is missed – Growth rates of data preclude traditional “manual intensive” approach: need automated data fusion techniques based on data mining  William M. Pottenger, Ph.D.
  6. 6. Why Do We Need Data Mining? • As databases and problems grow, the ability to support the decision support process using traditional query languages become infeasible – Many queries of interest are difficult to state in a query language (Query formulation problem) – “find all cases of fraud” – “find all individuals likely to buy a FORD Expedition” – “find all documents that are similar to this customers problem”  William M. Pottenger, Ph.D.
  7. 7. What (exactly) is Data Mining? • Let‟s take a few moments and consider this question. Is it: – Knowledge Discovery? – Knowledge Management? – Information Retrieval? – On-line Analytic Processing (OLAP)? – Machine Learning? – Decision Support? – Process Modeling/Control? –…  William M. Pottenger, Ph.D.
  8. 8. Definitions • Data mining is the application of computer technology and machine learning algorithms to discover patterns, anomalies, trends, and knowledge from data. – SGI Mineset Product Description • Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. – Data Mining by Witten and Frank • Data mining, also popularly referred to as knowledge discovery in databases (KDD), is the automated or convenient extraction of patterns representing knowledge implicitly stored in large databases, data warehouses, and other massive information repositories. – Data Mining: Concepts and Techniques by Han and Kamber  William M. Pottenger, Ph.D.
  9. 9. What is Text Mining? • Swanson („91) posed problem: Migraine headaches (M) – stress associated with M – stress leads to loss of magnesium – calcium channel blockers prevent some M – magnesium is a natural calcium channel blocker – spreading cortical depression (SCD) implicated in M – high levels of magnesium inhibit SCD – M patients have high platelet aggregability – magnesium can suppress platelet aggregability • All extracted from medical journal titles  William M. Pottenger, Ph.D. Slide reused with permission of Marti Hearst @ UCB
  10. 10. Gathering Evidence stress CCB magnesium migraine magnesium SCD PA magnesium magnesium  William M. Pottenger, Ph.D. Slide reused with permission of Marti Hearst @ UCB
  11. 11. Novel Discovery: Magnesium & Migraines! CCB migraine PA magnesium SCD stress No single author knew/wrote about this connection… this distinguishes Text Mining from Information Retrieval.  William M. Pottenger, Ph.D. Slide reused with permission of Marti Hearst @ UCB
  12. 12. Why Use Data Mining? • Data mining will become much more important, and companies will throw away nothing about their customers because it will be so valuable. If you’re not doing this, you’re out of business. – Arno Penzias, Chief Scientist @ Bell Labs • We are deluged by data – scientific data, medical data, demographic data, financial data, and marketing data. People have no time to look at this data. Human attention has become a precious resource. – Jim Gray, Microsoft Research in preface to Data Mining by Han and Kamber • Necessity is the mother of invention – Unknown   William M. Pottenger, Ph.D.
  13. 13. How is Data Mining Used? • Direct Marketing • Customer Acquisition • Customer Retention • Cross-selling • Trend Analysis • Fraud Detection • Forecasting in Financial Markets • Process Modeling • Process Control • …  William M. Pottenger, Ph.D.
  14. 14. But What is Data Mining (Really)? Copyright © 1997 Stiftelsen Østfoldforskning: Used with permission Data Mining: A Process  William M. Pottenger, Ph.D.
  15. 15. An Example of Data Mining in Process Modeling and Control at HP • Quality Assurance troubleshooting – KnowledgeSeeker Decision Tree Data Mining Tool identified critical factors impacting production of HP IIc Color Scanner • Process control – KnowledgeSeeker Decision Tree Data Mining Tool derived rules necessary to identify situations where process was about to go out of control.  William M. Pottenger, Ph.D.
  16. 16. How Do Decision Trees Work? Decision trees predict results but also tell about structure.  William M. Pottenger, Ph.D.
  17. 17. Be right back … A Demonstration of Data Mining Featuring KnowledgeSEEKER by Angoss Knowledge Engineering  William M. Pottenger, Ph.D.
  18. 18. Examples of Commercial Data Mining Systems • IBM‟s DB2 Intelligent Miner – • SAS Institute‟s Enterprise Miner – • SPSS‟s Clementine – • Angoss‟ KnowledgeSeeker – • Plus many more …  William M. Pottenger, Ph.D.
  19. 19. Asymptopia We are always given finite amounts of data … and rarely do we reach asymptopia. Asymptopia is the mythical land, the data miners 'utopia', where the amount of data is infinite and all algorithms converge and all users are satisfied ... Naturally, asymptopia can be reached only in the limit. Ron Kohavi Nuggets 96:21 (  William M. Pottenger, Ph.D.