Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Concept Data Analysis: Theory and Applications
Concept Data Analysis: Theory and Applications
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
A survey of Knowledge Discovery and Data Mining process models
The Knowledge Engineering Review
Hierarchies generated for data represented by fuzzy ternary relations
ICS'09 Proceedings of the 13th WSEAS international conference on Systems
Lattices for 3-dimensional fuzzy data generated by fuzzy Galois connections
WSEAS Transactions on Systems and Control
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Nowadays, we need much time and effort for extracting useful information in a flood of data, which are generated everyday by the development of a computer. In order to solve such a problem, many approaches are proposed. Data Mining is the whole process for knowledge discovery by analyzing data, or by extracting patterns in specific categories from data. As a Data Mining approach, in recent days, interests in Formal Concept Analysis and Rough Set Theory are on the increase and researches based on them are going in progress actively. In this paper, we have proposed a rough concept analysis and developed Rough Concept Analyzer for rough classification and extract hidden knowledge easily from given vague data. Also we have demonstrated how our proposed approach can be applied in tag-based social bookmarking system through our experiment. By rough classification using the "Rough Concept Analyzer", we can discover useful knowledge that cannot find out by using the FCA, from the vague data. Our tool would be helpful for rough classification and analyzing the uncertain data out of various fields.