62 research outputs found
Can opportunities be enhanced for vaccinating children in home visiting programs? A population-based cohort study
Does a pay-for-performance program for primary care physicians alleviate health inequity in childhood vaccination rates?
A study of the canopy top region in arrays of urban-type roughness combining laser Doppler velocimetry, flow visualisation and computational fluid dynamics to characterise the flow patterns and the canopy top exchange mechanisms
Mining Interesting Patterns Using Estimated Frequencies from Subpatterns and Superpatterns
Interestingness is not a dichotomy: Introducing softness in constrained pattern mining
Abstract. The paradigm of pattern discovery based on constraints was introduced with the aim of providing to the user a tool to drive the discovery process towards potentially interesting patterns, with the positive side effect of achieving a more efficient computation. So far the research on this paradigm has mainly focussed on the latter aspect: the development of efficient algorithms for the evaluation of constraint-based mining queries. Due to the lack of research on methodological issues, the constraint-based pattern mining framework still suffers from many problems which limit its practical relevance. As a solution, in this paper we introduce the new paradigm of pattern discovery based on Soft Constraints. Albeit simple, the proposed paradigm overcomes all the major methodological drawbacks of the classical constraint-based paradigm, representing an important step further towards practical pattern discovery. 1 Background and Motivations During the last decade a lot of researchers have focussed their (mainly algorithmic) investigations on the computational problem of Frequent Pattern Discovery, i.e. minin
Sociodemographic and health‐related determinants of seasonal influenza vaccination in pregnancy: A systematic review and meta‐analysis of the evidence since 2000
A Probabilistic Framework Towards the Parameterization of Association Rule Interestingness Measures
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