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Detecting Critical Least Association Rul by NilaamChow is a document available to read on EtoBox.

This paper discusses the mining of critical least association rules (ARs) in medical databases using a novel measurement called Critical Relative Support (CRS) and a scalable algorithm named Significant Least Pattern Growth (SLP-Growth). The authors demonstrate the effectiveness of their approach through experiments on two medical datasets, revealing the potential of detecting significant but infrequent ARs. The study highlights the importance of these rules in critical medical applications despite the chal

Author

NilaamChow

Language

EN