Scaling up disease model discovery

Scaling up cosine interesting pattern discovery: A depth-first method

This paper presents an efficient algorithm called CosMinert for interesting pattern discovery. The widely used cosine similarity, found to possess the null-invariance property and the anti-cross-support-pattern property, is adopted as the interestingness measure in CosMinert . CosMinert is generally an FP-growth-like depth-first traversal algorithm that rests on an important property of the cos...

Scaling Up the 3 Cs Model

The 3C model was rst developed in [Trac 89] and reviewed at last year's workshop [SMTR 90]. This position paper responds to that review, and in doing so outlines a framework for thinking about 3C aspects of an architecture of components. Such components can be said to be normalized in that they capture one concern of the architecture in their content.

Scaling Up

Scaling Up

Most analytical and experimental results on evolutionary programming (EP) are obtained using low-dimensional problems, e.g., smaller than 50. It is unclear, however, whether the empirical results obtained from the low-dimensional problems still hold for high-dimensional cases. This paper investigates the behaviour of four diierent EP algorithms for large-scale problems, i.e., problems whose dim...

Scaling up interventions for chronic disease prevention: the evidence.

Interventions to prevent morbidity and mortality from chronic diseases need to be cost effective and financially feasible in countries of low or middle income before recommendations for their scale-up can be made. We review the cost-effectiveness estimates on policy interventions (both population-based and personal) that are likely to lead to substantial reductions in chronic diseases--in parti...