Anytime exploratory data analysis for massive data sets
Padhraic Smyth, David H. Wolpert · Knowledge Discovery and Data Mining · 1997
Exploratory data is inherently an iterative, interactive endeavor. In the context of massive data sets, however, many current data algorithms will not scale appropriately to permit interaction on a human time-scale. In this paper anytime data analysis is proposed as a general framework to enable exploratory data of massive data sets. Anytime data takes into account not only the quality of the model being fit but also the resources (time and memory) used to achieve that fit. The framework is discussed in some detail for interactive multivariate density estimation. Out-of-sample log-likelihood and model combination techniques (such as stacking) are used to greedily explore the data landscape. The method is applied to two significant scientific data sets where it is shown that it can be better to combine multiple cheap-to-construct models than to spend the same time optimizing the parameters of a single more complex model.