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Data Mining Web Sites

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Data Mining Web Sites. Plan of this week Benefits and incentives Learning and evolving Steps to mining web data Techniques and algorithms. Plan of this Week. Monday : Web mining Notes by Margaret Dunham, Ch. 8 Web mining (part III)

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Data Mining Web Sites

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  1. Data Mining Web Sites Plan of this week Benefits and incentives Learning and evolving Steps to mining web data Techniques and algorithms

  2. Plan of this Week • Monday: Web mining • Notes by Margaret Dunham, Ch. 8 Web mining (part III) • Notes based on “Data mining your website” by Jesus Mena with focus on E-commerce applications • Presentation by Donghui Wu “Overview of Web Analytics with Example” • Wednesday: Paper presentations on Web mining + Text mining

  3. Benefits and incentives • Billions of business transactions flow and evolve, transforming consumers and retailers in web based dynamic marketplace and the relationships between them. • Mining of website data with AI-based tools (programs designed to mimic human functions). For example, to recognize, anticipate, and learn buying habits and preferences of customers.

  4. Portal: From Idle to Intelligent • From data accumulating to data mining: visitor data gathered every hour of every day insight of business • Data mining – inductive data analysis • Data mining – not query or user-driven, nor a cumulative traffic report • Combination of resource (logs file and database info) and techniques

  5. The AI War • Two main schools of thought on how machines should learn: inductive and deductive analysis. • Deductive: Expert => rules => knowledge, top-down approach, expert systems used LISP, Prolog, and shell languages CLIPS and JESS; programs suffered from: brittle and expensive to maintain • Inductive: knowledge <= rules <= Data, bottom-up, machine learning and data mining – extracts patterns from data and learns from examples, such as DT, ANN, GA; starting from 1980’s

  6. Case study:Wal-Mart • One of the world’s largest data mining applications • Individually profile every one their 3000+ stores 52 weeks a year for product demands on over 700 million unique store/item combinations  reduce overhead, inventory costs and stock

  7. How data mining answers key business questions

  8. How data mining answers key business questions(cont.)

  9. 10 steps to mine web data • Identify your objective – profile your visitors? • Select your data – form database? • Prepare the data – append demographic information? • Evaluate the data – visualization? • Format the solution – segmentation, prediction?

  10. 10 steps to mine web data (cont.) 6. Select the tool – self-coding, existing tool? • Construct the models – train and test? • Validate the findings – share with teams? • Deliver the findings – provide report, code? • Integrate the solution – marketing campaign?

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