Chapter

Domain Driven Data Mining

pp 171-180

Date:

Post Mining

  • Longbing CaoAffiliated withFac. Engineering & Information Tech. Centre for Quantum Computation and Intelligent Systems, University of Technology, Sydney Email author 
  • , Chengqi ZhangAffiliated withFac. Engineering & Information Tech. Centre for Quantum Computation and Intelligent Systems, University of Technology, Sydney
  • , Philip S. YuAffiliated withDepartment of Computer Science, University of Illinois, Chicago
  • , Yanchang ZhaoAffiliated withFac. Engineering & Information Tech. Centre for Quantum Computation and Intelligent Systems, University of Technology, Sydney

* Final gross prices may vary according to local VAT.

Get Access

Abstract

Data mining is widely used in many areas, such as retail, telecommunication, finance, etc. However, many data miners often face the following problems: How to read and understand discovered patterns, which are often in thousands or more? What are the most interesting ones? Is the model accurate and what does the model tell us? How to use the rules, patterns and models? To answer the above questions and present useful knowledge to users, it is necessary to do post mining to further analyse the learned patterns, evaluate the built models, refine and polish the built models and discovered rules, summarize them, and use visualisation techniques to make them easy to read and understand [242]. The function of post-mining in knowledge discovery process is shown in Figure 8.1, which bridges the gap between the patterns discovered by data mining techniques and the useful knowledge desired by end users.