A b c d e ms xg ng ws a b c d e tansteinbach kumar

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(A B) (C) (D E) <= ms <= xg >ng <= ws (A B) (C) (D E)
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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 34 Sequential Pattern Discovery: Examples In telecommunications alarm logs, (Inverter_Problem Excessive_Line_Current) (Rectifier_Alarm) --> (Fire_Alarm) In point-of-sale transaction sequences, Computer Bookstore: (Intro_To_Visual_C) (C++_Primer) --> (Perl_for_dummies,Tcl_Tk) Athletic Apparel Store: (Shoes) (Racket, Racketball) --> (Sports_Jacket)
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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 35 Regression Predict a value of a given continuous valued variable based on the values of other variables, assuming a linear or nonlinear model of dependency. Greatly studied in statistics, neural network fields. Examples: Predicting sales amounts of new product based on advetising expenditure. Predicting wind velocities as a function of temperature, humidity, air pressure, etc. Time series prediction of stock market indices.
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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 36 Deviation/Anomaly Detection Detect significant deviations from normal behavior Applications: Credit Card Fraud Detection Network Intrusion Detection Typical network traffic at University level may reach over 100 million connections per day
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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 37 Challenges of Data Mining Scalability Dimensionality Complex and Heterogeneous Data Data Quality Data Ownership and Distribution Privacy Preservation Streaming Data
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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 38 June 3, 2013 Data Mining: Concepts and Techniques 38 Architecture: Typical Data Mining System data cleaning, integration, and selection Database or Data Warehouse Server Data Mining Engine Pattern Evaluation Graphical User Interface Know ledge -Base Database Data Warehouse World-Wide Web Other Info Repositories
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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 39
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