Data-Mining-ppt (1).pdf
- 1. www.studymafia.org Submitted To: Submitted By: www.studymafia.org www.studymafia.org Seminar On Data Mining
- 2. Content Data Mining Data Mining Definition Data Mining – Two Main Components Data Mining vs. Data Analysis What is (not) Data Mining? Related Fields Data Mining Process Major Data Mining Tasks Uses of Data Mining Sources of Data for Mining Challenges of Data Mining Advantages Conclusion Reference
- 3. DataMining New buzzword, old idea. Inferring new information from already collected data. Traditionally job of Data Analysts Computers have changed this. Far more efficient to comb through data using a machine than eyeballing statistical data.
- 4. DataMining Definition Data mining in Data is the process of identifying potentially and ultimately in data.
- 5. DataMining vs. DataAnalysis In terms of software and the marketing thereof Data Mining != Data Analysis Data Mining implies software uses some intelligence over simple grouping and partitioning of data to infer new information. Data Analysis is more in line with standard statistical software (ie: web stats). These usually present information about subsets and relations within the recorded data set (ie: browser/search engine usage, average visit time, etc. )
- 6. What is(not) DataMining? Look up phone number in phone directory Query a Web search engine for information about “ Amazon” •Certain names are more prevalent in certain US locations (O’ Brien, O’ Rurke, O’ Reilly… in Boston area) • Group together similar documents returned by search engine according to their context (e.g. Amazon rainforest, Amazon.com,) Whatis notDataMining? Whatis DataMining?
- 8. Why Mine Data?Scientific Viewpoint ⚫ Data collected and stored at enormous speeds (GB/hour) o remote sensors on a satellite o telescopes scanning the skies o microarrays generating gene expression data o scientific simulations generating terabytes of data ⚫ Traditional techniques infeasible for raw data ⚫ Data mining may help scientists o in classifying and segmenting data o in Hypothesis Formation
- 10. Related Fields Statistics Machine Learning Databases Visualization DataMining and Knowledge Discovery
- 12. Major DataMiningTasks Classification: predicting an item class Associations: e.g. A & B & C occur frequently Visualization: to facilitate human discovery Estimation: predicting a continuous value Deviation Detection: finding changes Link Analysis: finding relationships...
- 13. Usesof DataMining AI/Machine Learning Good for analyzing winning strategies to games, and thus developing intelligent AI opponents. (ie: Chess) Business Strategies Identify customer demographics, preferences, and purchasing patterns. Risk Analysis Analyze product defect rates for given plants and predict possible complications (read: lawsuits) down the line.
- 14. Usesof DataMining(Cont..) User Behavior Validation In the realm of cell phones Comparing phone activity to calling records. Can help detect calls made on cloned phones. Similarly, with credit cards, comparing purchases with historical purchases. Can detect activity with stolen cards.
- 15. Usesof DataMining(Cont..) Health and Science Predicting protein interactions and functionality within biological cells. Applications of this research include determining causes and possible cures for Alzheimers, Parkinson's, and some cancers (caused by protein "misfolds") Scanning Satellite receptions for possible transmissions from other planets. For more information see Stanford’ s Folding@home and SETI@home projects. Both involve participation in a widely distributed computer application.
- 16. Sourcesof Datafor Mining Databases (most obvious) Text Documents Computer Simulations Social Networks
- 17. Advantagesof DataMining Marketing / Retail Finance / Banking Manufacturing Governments
- 18. Challenges of DataMining Scalability Dimensionality Complex and Heterogeneous Data Data Quality Data Ownership and Distribution Privacy Preservation Streaming Data
- 19. Conclusion Comprehensive data warehouses that integrate operational data with customer, supplier, and market information have resulted in an explosion of information. Competition requires timely and sophisticated analysis on an integrated view of the data. However, there is a growing gap between more powerful storage and retrieval systems and the users’ ability to effectively analyze and act on the information they contain.
- 21. Thanks
- 22. Queries?