Disjunctive rules mining from uncertain databases
Manal Alharbi, Priya Periaswamy, Sanguthevar Rajasekaran · 2014
Association rules mining is a well studied problem. Algorithms have been proposed for mining from uncertain data. In this paper we investigate the important problem of disjunctive rules mining from uncertain data. Specifically, we present an elegant algorithm for this problem and evaluate its performance on various datasets. This algorithm can be specialized to work with data without uncertainty and produce disjunctive rules. In this case the resultant algorithm is much simpler (while having a similar asymptotic run time) than the algorithms proposed in the literature.