New ways to present the results of a data mining process to an expert
Petra Perner
Institute of Computer Vision and applied Computer Sciences, IBaI, Germany
: J Comput Eng Inf Technol
Abstract
Data mining methods can easily work on applications with many features and come up with decision rules, relations or patterns for the application. The results should be reported in such a way that an expert can overlook them. Often it is forgotten that the quality of the presentation depends on the kind of attributes you are using, for example, the decision tree induction method. Therefore, attribute construction or summarization in a way that many attributes represent a feature is necessary. We show on different applications as to how this can be done and what quality of representation can be achieved. We also show what kind of representation binary and n-ary decision tree induction methods can bring out.
Biography
Petra Perner (IAPR Fellow) is the Director of the Institute of Computer Vision and Applied Computer Sciences, IBaI. She received her Diploma Degree in Electrical Engineering and her PhD Degree in Computer Science for her work on “Data reduction methods for industrial robots with direct teach-in-programing”. Her Habilitation thesis was about “A methodology for the development of knowledge-based image-interpretation systems". She has been the Principal Investigator of various national and international research projects. She received several research awards for her research work and has been awarded with three business awards for her work on bringing intelligent image interpretation methods and data mining methods into business. Her research interests are in Image Analysis and Interpretation, Machine Learning, Data Mining, Big Data, Machine Learning, Image Mining and Case-Based Reasoning.
E-mail: pperner@ibai-institut.de
