Complementary usage of OLAP and data mining represented by GUHA procedures

This master thesis explores the possibilities for a complementary usage of OLAP and data mining represented by GUHA procedures. One of the main goals is to propose particular scenarios for its implementation, covering several GUHA procedures as part of data mining. Apart from this goal, the work also aims to design a general methodology helping with application of mentioned synergy. The data analysis is performed using Tableau and Lisp-Miner tools. In terms of data mining, LISp-Miner, CF-Miner, SD4ft-Miner and Ac4ft-Miner procedures are used. The whole process follows the CRISP-DM methodology. The theoretical part provides a brief description of the basic concepts and principles associated with both methods, their comparison and current research on the selected topic. The practical part demonstrates an accomplishment of the thesis’s goals. For practical implementation, Los Angeles crime data was used. The contributions of this thesis include: a proposal of scenarios covering complementary usage of CF-Miner, SD4ft-Miner and Ac4ft-Miner procedures and OLAP; a recommendation related to the usage of selected GUHA procedure in terms of these scenarios, and the design of a general methodology for an application of the mentioned synergy.