Data-Mining-ppt.pptx

Data-Mining-ppt.pptx

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  www.studymafia.org Submitted To: SubmittedBy: www.studymafia.org www.studymafia.org Seminar On Data Mining
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  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
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  Data Mining  Newbuzzword, 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.
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  Data Mining Definition Datamining in Data is the non-trivial process of identifying  valid  novel  potentially useful  and ultimately understandable patterns in data.
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  Data Mining vs.Data Analysis  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. )
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  What is (not)Data Mining? 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,) What is not Data Mining? What is Data Mining?
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  Data Mining Techniques Classification  Clustering  Regression  Association Rules
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  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
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  Data Mining Architecture
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  Related Fields Statistics Machine Learning Databases Visualization Data Miningand Knowledge Discovery
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  __ __ __ __ __ __ __ __ __ Transformed Data Patterns and Rules Target Data Raw Data Knowledge Interpretation & Evaluation Integration Understanding Data MiningProcess DATA Ware house Knowledge
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  Major Data MiningTasks  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...
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  Uses of DataMining  AI/Machine Learning Combinatorial/Game Data Mining Good for analyzing winning strategies to games, and thus developing intelligent AI opponents. (ie: Chess)  Business Strategies Market Basket Analysis Identify customer demographics, preferences, and purchasing patterns.  Risk Analysis Product Defect Analysis Analyze product defect rates for given plants and predict possible complications (read: lawsuits) down the line.
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  Uses of DataMining (Cont..)  User Behavior Validation Fraud Detection 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.
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  Uses of DataMining (Cont..)  Health and Science Protein Folding 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") Extra-Terrestrial Intelligence 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.
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  Sources of Datafor Mining  Databases (most obvious)  Text Documents  Computer Simulations  Social Networks
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  Advantages of DataMining  Marketing / Retail  Finance / Banking  Manufacturing  Governments
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  Challenges of DataMining  Scalability  Dimensionality  Complex and Heterogeneous Data  Data Quality  Data Ownership and Distribution  Privacy Preservation  Streaming Data
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  Conclusion  Comprehensive datawarehouses 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.
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  Reference  www.google.com  www.wikipedia.com www.studymafia.org
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  Thanks
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  Queries?