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Data Mining Web Sites

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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