Page 178 - DCAP208_Management Support Systems

Unit 10: Data Mining Tools and Techniques

Notes

Task Compare and contrast text mining and web mining.

Self Assessment

Fill in the blanks:

13. ................... is a form of information harvesting that applies to data gathered from online
sources.
14. Web mining activities focus on ................... information, rather than a large cross section of
information sources.
15. ................... provides detailed information about a specific website’s internal structure.

Case Study Data Mining in Mobile Communication

Application Background
Mobile communication data analysis has been often used as a background application to
motivate many technical problems in data mining research, such as mining frequent
patterns and clusters on data streams, social network analysis, collaborative filtering and
recommendation. However, very few data mining researchers have a chance to see a
working data mining system on real mobile communication data. The lack of this
experience prevents those researchers from deeply understanding the business application
scenarios in mobile communication as well as the successes and the limitations of the
existing techniques.
We are developing MobileMiner, a data mining tool for mobile data analysis and business
strategy development. Built on the state-of-the-art data mining techniques, MobileMiner
presents a real case study on how to integrate data mining techniques into a business
solution. In a large mobile communication company like China Mobile Communication
Corporation, there are many analytical tasks where data mining can help to address the
business interests of the company. Clearly, a system cannot cover all aspects. MobileMiner
starts with customer relation management, the core component of mobile communication
business. In this demo, we focus on two tasks, mobile user segmentation and community
discovery from user calling networks.
MobileMiner provides a platform for the analytical tasks, where user profiles are extracted
continuously from users’ moving and calling records. The profiles are extremely important
and valuable in business. Based on the profile mining platform, various data mining tasks
can be effectively performed using different features of the profiles. The mobile user
segmentation task tries to group customers by their frequent moving patterns. The features
used for grouping are obtained by mining users’ moving records continuously on the
profile mining platform. Knowing the moving patterns for different customer groups, a
service provider can dynamically deploy resources to improve the service quality (e.g.,
adjusting the angles of antennas or re-positioning a mobile station). For example, in
Beijing Olympic period, many people are moving from Bird Nest around 9pm to Olympic
Village around 11pm. It is interesting to find the clusters of customers in terms of service
areas and time.

Contd....

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