— Uncertainties in data arise for a number of reasons: when the data set is incomplete, contains conflicting information or has been deliberately perturbed or coarsened to remov...
Graham Cormode, Divesh Srivastava, Entong Shen, Ti...
The single minimum support (minsup) based frequent pattern mining approaches like Apriori and FP-growth suffer from“rare item problem”while extracting frequent patterns. That...
Though there exist some rules of thumb for design of good models for solving constraint satisfaction problems, the modeling process still belongs more to art than to science. Moreo...
While many real-world combinatorial problems can be advantageously modeled and solved using Constraint Programming, scalability remains a major issue in practice. Constraint models...
Kenneth M. Bayer, Martin Michalowski, Berthe Y. Ch...
Constraint Programming is an attractive approach for solving AI planning problems by modelling them as Constraint Satisfaction Problems (CSPs). However, formulating effective cons...
Andrea Rendl, Ian Miguel, Ian P. Gent, Peter Grego...