Introduction to Data Mining

Introduction to Data Mining

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  An Introduction toSQL Server 2005 and 2008 Data Mining Peter Myers Mentor – Solid Quality Mentors San Francisco SQL Server User Group – 8 July, 2009 Copyright © 2009, Solid Quality Mentors. All rights reserved.
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  WHO WE ARE •Industry experts: Growing, elite group of over 90 of the world’s best technical experts who, as reflected by the high concentration of Microsoft MVP’s and RD’s in our ranks, achieve excellence in their industry by maintaining the highest credentials. • Published authors: Best technical reference books, Microsoft reference materials, industry white papers, technical magazine articles, and webcasts. • Top technical speakers: PASS Community Summit, Microsoft TechEd, The Microsoft BI Conference, SQL Server DevConnections, countless user groups, international conferences and events. • For more information visit www.solidq.com Copyright © 2009, Solid Quality Mentors. All rights reserved.
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  WHAT WE DO Provideadvanced, world-class expertise across the entire Microsoft relational data and development platforms and complimenting technologies. PRACTICE AREAS SERVICES Relational Database Management Advanced, Public Training Business Intelligence Customized, Private Training Development Methodologies Solution Delivery & Tuning SharePoint Collaboration Enhanced, Mentoring Services For more information visit www.solidq.com Copyright © 2009, Solid Quality Mentors. All rights reserved.
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  AGENDA • Introducing DataMining • Describing the Data Mining Process • SQL Server™ 2008 Data Mining • Data Preparation • Data Mining Visualization • Demonstrations Copyright © 2009, Solid Quality Mentors. All rights reserved.
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  INTRODUCING DATA MINING • Addresses the problem: “Too much data and not enough information” • Enables data exploration, pattern discovery, and pattern prediction—which lead to knowledge discovery • Forms a key part of a BI solution Copyright © 2009, Solid Quality Mentors. All rights reserved.
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  BUSINESS SCENARIOS • Identifying responsive customers/unresponsive customers (also known as churn analysis) • Targeting promotions • Detecting and preventing fraud • Correcting data during ETL • Forecasting sales and inventory • Cross-selling Copyright © 2009, Solid Quality Mentors. All rights reserved.
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  DESCRIBING THE DATA MINING PROCESS “Doing Data Mining” Business Data Understanding Understanding Data Preparation Data Deployment Modeling Evaluation “Putting Data Mining to Work” www.crisp-dm.org Copyright © 2009, Solid Quality Mentors. All rights reserved.
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  DATA PREPARATION • Often significant amounts of effort are required to prepare data for mining: • Transforming for cleaning and reformatting • Isolating and flagging abnormal data • Appropriately substituting missing values • Discretizing continuous values into ranges • Normalizing values between 0 and 1 • Of course, having the required data to begin with is important: • When designing systems, give consideration to attributes that may be required as inputs for classification o For example, demographic data: Age, Gender, Region, etc Copyright © 2009, Solid Quality Mentors. All rights reserved.
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  MODELING Design time Process time Querytime Mining Model Copyright © 2009, Solid Quality Mentors. All rights reserved.
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  MODELING Design time Process time Querytime Mining Model Data Mining Engine Training Data Copyright © 2009, Solid Quality Mentors. All rights reserved.
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  MODELING Design time Process time Querytime Mining Model Data Mining Engine Predicted Data Data to Predict Copyright © 2009, Solid Quality Mentors. All rights reserved.
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  MODEL VALIDATION • It is important that the model makes sense • Accuracy o Does it correlate and predict correctly? • Reliability o Does it work similarly for different test data? • Usefulness o Does it provide insight or only obvious trivialities? • Commonly a holdout set of data is used to test model accuracy Copyright © 2009, Solid Quality Mentors. All rights reserved.
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  SQL SERVER™ 2008 DATA MINING • Hides the complexity of an advanced technology • Includes full suite of algorithms to automatically extract information from data • Handles large volumes of data and complex data • Data can be sourced from relational and OLAP databases • Uses standard programming interfaces: • XMLA • DMX • Delivers a complete framework for building and deploying intelligent applications Copyright © 2009, Solid Quality Mentors. All rights reserved.
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  SQL SERVER™ 2008 ALGORITHMS • Microsoft Naïve Bayes • Quick and approachable algorithm • Used for classification • Microsoft Decision Trees • Popular data mining technique • Used for classification, regression and association • Microsoft Linear Regression • Finds the best possible straight line through a series of points • Used for prediction analysis Copyright © 2009, Solid Quality Mentors. All rights reserved.
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  SQL SERVER™ 2008 ALGORITHMS • Microsoft Neural Network • More sophisticated than Decision Trees and Naïve Bayes, this algorithm can explore extremely complex scenarios • Used for classification and regression tasks • Microsoft Logistic Regression • A particular case of the Neural Network algorithm • Microsoft Clustering • Finds natural groupings inside data • Supports segmentation and anomaly detection tasks Copyright © 2009, Solid Quality Mentors. All rights reserved.
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  SQL SERVER™ 2008 ALGORITHMS • Microsoft Sequence Clustering • Groups a sequence of discrete events into natural groups based on similarity • Microsoft Time Series • Used to predict future values from a time series • Has been improved in SQL Server 2008 to produce more accurate long-term forecasts • Microsoft Association Rules • Commonly supports market basket analysis to learn what products are purchased together Copyright © 2009, Solid Quality Mentors. All rights reserved.
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  SQL SERVER™ 2008 ALGORITHMS Classify Estimate Cluster Forecast Associate • Decision • Decision • Clustering • Time Series • Association Trees Trees Rules • Logistic • Linear • Decision Regression Regression Trees • Naïve • Logistic Bayes Regression • Neural • Neural Networks Networks Copyright © 2009, Solid Quality Mentors. All rights reserved.
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  DATA MINING VISUALIZATION • In contrast to OLTP and OLAP queries, data mining queries typically extract information that the user is not aware of • Appreciate that end users do not typically query data mining models directly • Visualizations can effectively present data discoveries • SQL Server™ 2008 provides algorithm-specific visualizations that can: • Test and explore models in BIDS • Be embedded into Web and Windows Forms applications • Developers can construct and plug-in custom data mining viewers Copyright © 2009, Solid Quality Mentors. All rights reserved.
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  DEMONSTRATIONS 1. Introducing the Table Analysis Tools for Excel 2007 2. Creating, Training, Testing and Querying Mining Models with BIDS 3. Developing a Data Mining Report 4. Enhancing an E-Commerce Site with Market Basket Analysis Copyright © 2009, Solid Quality Mentors. All rights reserved. 2008,
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  RESOURCES • www.microsoft.com/sql/technologies/dm • Links to technical resources, case studies, news, and reviews • www.sqlserverdatamining.com • Site designed and maintained by the SQL Server Data Mining team • Includes: Live samples, tutorials, webcasts, tips and tricks, and FAQ • Data Mining for SQL Server 2008, by ZhaoHui Tang and Jamie MacLennan Copyright © 2009, Solid Quality Mentors. All rights reserved.