 |
Polish computer scientist Pawlak in 1982 presented a tool for knowledge discovery, named as, Rough Set Theory. It is one of the first non-statistical approaches in data analysis. Rough set theory is based on set theory of mathematics. It can be used for feature selection, feature extraction, data reduction, decision rule generation, and pattern extraction (templates, association rules) etc.
The concept of the rough set theory can be applied in various fields such as:
Data Mining: Rough set theory can be applied to the field of data mining in different phases of knowledge discovery such as selection of core attribute, attribute extraction, reducing the datasets, generate the decision rule and pattern recognition.
Economic and financial prediction: It requires the accurate prediction of the future states based on the identification of patterns in the historical data
Medical Imaging: Such as medical image segmentation and object extraction, feature extraction/reduction, and image classification
Bio Informatics: Rough set-based rule induction allows easily interpretable descriptions of complex biological systems
|
IEEE/ICACT20220156 Slide.02
[Big Slide]