Authors: Avichai Meged, Roy Gelbard
Tags: 2012, conceptual modeling
A novel fuzzy data representation model which enables data mining with standard tools is introduced. Many data elements in the world are fuzzy in nature. There is an obvious need to represent and process such data effectively and efficiently, using the same standard tools for crisp data that are popular with researchers and practitioners alike. Currently, however,standard tools cannot process or analyze data that are not adequately represented. The comprehensive data representation model put forward here extends principles of binary databases and provides a unified approach to all types of data: discrete and continuous, crisp and fuzzy. The model is illustrated on a baseline dataset and tested in clustering experiments matched against controlled groupings and a real dataset. The tests confirm that the implementation of the model not only enables the use of standard tools but also yields better results as regards segmentation and clustering of fuzzy datasets.Read the full paper here: https://www.igi-global.com/journal/journal-database-management
