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Data mining slides

  1. 1. Data Mining Presented By: Sarfaraz M Manik Making Sense Of Data
  2. 2. Data, Information & Knowledge <ul><li>Data </li></ul><ul><ul><li>The most elementary descriptions of things, events, activities, and transactions </li></ul></ul><ul><li>Information </li></ul><ul><ul><li>Organized data that has meaning and value </li></ul></ul><ul><li>Knowledge </li></ul><ul><ul><li>The concept of understanding information based on recognized patterns in a way that provides insight to information. </li></ul></ul>
  3. 4. Business Intelligence <ul><ul><li>Business intelligence ( BI ) refers to applications and technologies which are used to gather, provide access to, and analyze data and information about their company operations. </li></ul></ul><ul><ul><li>Data Mining is an important part of Business Intelligence. </li></ul></ul>
  4. 5. Business Intelligence & Data Mining IT Business Intelligence Behavioral Biases Models Tools Methods Data Decision Problems
  5. 6. What is Data Mining? <ul><li>Data mining: The task of discovering interesting patterns from large amounts of data </li></ul>
  6. 7. Data Mining : Confluence of Multiple Disciplines Data Mining Database Technology Statistics Other Disciplines Information Science Machine Learning Visualization
  7. 8. Data, Data everywhere yet … <ul><li>I can’t find the data I need </li></ul><ul><li>I can’t get the data I need </li></ul><ul><li>I can’t understand the data I found </li></ul><ul><li>I can’t use the data I found </li></ul>Why Data Mining?
  8. 9. Why Data Mining? <ul><li>Data explosion problem </li></ul><ul><ul><li>Advance data collection tools and database technology lead to tremendous amounts of data stored in databases. </li></ul></ul><ul><li>We are drowning in data, but starving for knowledge! </li></ul><ul><li>Solution: Data warehousing and data mining </li></ul><ul><ul><li>Data warehousing and on-line analytical processing </li></ul></ul><ul><ul><li>Extraction of interesting knowledge using Data Mining </li></ul></ul>
  9. 10. Application Of Data Mining Industry Application Finance Credit Card Analysis Insurance Claims, Fraud Analysis Telecommunication Call record analysis Transport Logistics management Consumer goods promotion analysis Scientific Research Image, Video, Speech Utilities Power usage analysis
  10. 11. Steps of Data Mining <ul><li>Data integration </li></ul><ul><li>Data selection </li></ul><ul><li>Data cleaning </li></ul><ul><li>Data transformation </li></ul><ul><li>Data mining </li></ul><ul><li>Pattern evaluation </li></ul><ul><li>Knowledge presentation </li></ul>
  11. 12. Steps of Data Mining
  12. 13. Data Mining Techniques <ul><li>Classification and Prediction </li></ul><ul><ul><li>Example - Focused Hiring </li></ul></ul><ul><li>Cluster Analysis </li></ul><ul><ul><li>Example - Market Segmentation </li></ul></ul><ul><li>Outlier Analysis </li></ul><ul><ul><li>Example - Fraud Detection </li></ul></ul><ul><li>Association Analysis </li></ul><ul><ul><li>Example - Market Basket Analysis </li></ul></ul><ul><li>Evolution Analysis </li></ul><ul><ul><li>Example – Forecasting stock market index using Time Series Analysis </li></ul></ul>
  13. 14. Is Data Mining a threat to Privacy and Information Security? <ul><li>Solutions: </li></ul><ul><li>Purpose Specifications & Use Limitation </li></ul><ul><li>Openness </li></ul><ul><li>Security Measures like encryption </li></ul>
  14. 15. Data Warehousing <ul><li>A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management’s decision making process. </li></ul><ul><li>The most common form of data integration. </li></ul><ul><ul><li>Copy sources into a single database and try to keep it up-to-date. </li></ul></ul><ul><ul><li>Usual method: periodic reconstruction of the warehouse, perhaps overnight. </li></ul></ul><ul><ul><li>Frequently essential for analytic queries. </li></ul></ul>
  15. 16. Data Warehouse Customers Etc… Vendors Etc… Orders Data Warehouse Enterprise “ Database” Transactions Copied, organized summarized Data Mining <ul><li>Data Miners: </li></ul><ul><li>“ Farmers” – they know </li></ul><ul><li>“ Explorers” - unpredictable </li></ul>
  16. 17. Data Warehousing <ul><li>Data Warehousing provides the Enterprise with a memory </li></ul><ul><li>Data Mining provides the Enterprise with intelligence </li></ul>
  17. 18. OLTP & OLAP <ul><li>On-Line Transaction Processing (OTLP): </li></ul><ul><ul><li>Short, simple, frequent queries and/or modifications, each involving a small number of tuples. </li></ul></ul><ul><ul><li>Examples: Answering queries from a Web interface, sales at cash registers, selling airline tickets. </li></ul></ul><ul><li>On-Line Application Processing (OLAP): </li></ul><ul><ul><li>Few, but complex queries --- may run for hours. </li></ul></ul><ul><ul><li>Queries do not depend on having an absolutely up-to-date database. </li></ul></ul><ul><ul><li>Examples: Analysts at Wal-Mart look for items with increasing sales in some region. </li></ul></ul>
  18. 19. Data Mining Tools <ul><ul><li>Microsoft SQL Server 2005 </li></ul></ul><ul><ul><li>Microsoft SQL Server 2008 </li></ul></ul><ul><ul><li>Oracle Data Mining </li></ul></ul><ul><ul><li>DBMiner </li></ul></ul>
  19. 20. <ul><li> Thanks To All </li></ul>