lec7 - CS 6093 Lecture 7 Spring 2011 Basic Data Mining Cong...

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CS 6093 Lecture 7 Spring 2011 Basic Data Mining Cong Yu 03/21/2011
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Announcements No regular office hour next Monday (March 28 th ) Office hour next week will be on Tuesday through Thursday by appointment only I will be out of town from April 1 st to April 16 th Sporadic email access, please plan accordingly if you need to discuss your projects with me Midterm report will be graded soon We are aiming for the end of the week Quiz next week will be based on today’s lecture And it will be closed notes Any question on the projects?
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Today’s Outline Overview of Data Mining What, Why, How Classic Studies Association Rule Mining Data Cube Analysis Rule Interestingness
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What is Data Mining? You are familiar with Data/Information Retrieval Querying the database using SQL Search the Web via keyword queries Data mining is NOT data retrieval Data mining = Discover hidden and useful knowledge from large amounts of data Hidden : you can’t easily write a query to fetch what you want … because you don’t even know what you want Interesting : not every piece of hidden knowledge is useful ... trivial discoveries can overwhelm the user Large amounts : simple techniques are no longer sufficient … need efficient and scalable techniques
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Examples of Data Mining Results Rules and Patterns Customers who buy Harry Potter books often buy Twilight books Users in NYC tend to search for expensive restaurants on Valentines Day Clusters and Classification TV viewers who watch 2+ hours of cable news every day can be divided into three groups: CNN, MSNBC, and Fox Given a viewer, predict which group s/he falls into (for advertising purpose)
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Why Study Data Mining? Lots of Data Opportunities for: Purchase recommendation Credit card fraud detection Challenging for: Hidden information detection beyond human eyes Efficiency and scalability Amazon, Walmart, Citibank, etc.
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How Data Mining Become a Field Started within the Database Systems community OLAP instead of OLTP OLTP: online transactional processing ATM transactions, Shopping transactions, etc. OLAP: online analytical processing Business intelligence, business reporting Heavily influenced by Machine Learning – Statistics More recently Information Retrieval Recommendation Systems
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How is Data Mining Done? Descriptive (closer to database): Classic topics: Association rule mining Frequent pattern mining Data cube analysis – Clustering Group similar data points and separate dissimilar data points Anomaly detection Detect data points that significantly deviates from others Predictive (closer to statistics and machine learning): – Classification Predict which label to be assigned to a data point based on its features Regression analysis Predict the value of a dependent variable (e.g., sales) based on the underlying variables (e.g., time and location)
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