Analysis of association rule extraction between rough set and concept lattice

The model of concept lattice has strong ability of knowledge representation and knowledge discovery. Rough set theory based on the attribute reduction method often inevitably cuts out some useful information. Concept lattice, by contrast, has the relative completeness in association rule mining, and is user-friendly to find interesting information. So it can improve the mining efficiency. Based on the summaries of several typical attribute reduction algorithms, the thesis extracts association rules from the decision table, and shows that concept lattice can better realize the intuitive visualization in the process of association rule mining.

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