Yes married 50k no married 150k yes divorced 90k no

Info icon This preview shows pages 17–23. Sign up to view the full content.

View Full Document Right Arrow Icon
Yes Married 50K ? No Married 150K ? Yes Divorced 90K ? No Single 40K ? No Married 80K ? Test Set Training Set Model Learn Classifier
Image of page 17

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 18 Classification: Application 1 Direct Marketing Goal: Reduce cost of mailing by targeting a set of consumers likely to buy a new cell-phone product. Approach: Use the data for a similar product introduced before. We know which customers decided to buy and which decided otherwise. This {buy, don’t buy} decision forms the class attribute . Collect various demographic, lifestyle, and company- interaction related information about all such customers. Type of business, where they stay, how much they earn, etc. Use this information as input attributes to learn a classifier model. From [Berry & Linoff] Data Mining Techniques, 1997
Image of page 18
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 19 Classification: Application 2 Fraud Detection Goal: Predict fraudulent cases in credit card transactions. Approach: Use credit card transactions and the information on its account-holder as attributes. When does a customer buy, what does he buy, how often he pays on time, etc Label past transactions as fraud or fair transactions. This forms the class attribute. Learn a model for the class of the transactions. Use this model to detect fraud by observing credit card transactions on an account.
Image of page 19

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 20 Classification: Application 3 Customer Attrition/Churn: Goal: To predict whether a customer is likely to be lost to a competitor. Approach: Use detailed record of transactions with each of the past and present customers, to find attributes. How often the customer calls, where he calls, what time-of-the day he calls most, his financial status, marital status, etc. Label the customers as loyal or disloyal. Find a model for loyalty. From [Berry & Linoff] Data Mining Techniques, 1997
Image of page 20
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 21 Classification: Application 4 Sky Survey Cataloging Goal: To predict class (star or galaxy) of sky objects, especially visually faint ones, based on the telescopic survey images (from Palomar Observatory). 3000 images with 23,040 x 23,040 pixels per image. Approach: Segment the image. Measure image attributes (features) - 40 of them per object. Model the class based on these features. Success Story: Could find 16 new high red-shift quasars, some of the farthest objects that are difficult to find! From [Fayyad, et.al.] Advances in Knowledge Discovery and Data Mining, 1996
Image of page 21

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 22 Classifying Galaxies Early Intermediate Late Data Size:  72 million stars, 20 million galaxies Object Catalog: 9 GB Image Database: 150 GB   Class:  Stages of Formation Attributes: Image features,  Characteristics of light  waves received, etc.
Image of page 22
Image of page 23
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern