© The Author(s) 2008. This article is published with open access at Springerlink.com Abstract Process mining includes the automated discovery of processes from event logs. Based on observed events (e.g., activities being executed or messages being exchanged) a process model is constructed. One

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Systems for inducing concept descriptions from examples are valuable tools for assisting in the task of knowledge acquisition for expert systems. This paper presents a description and empirical evaluation of a new induction system, cn2, designed for the efficient induction of simple

It is widely known that when there are errors with a moving-average root close to −1, a high order augmented autoregression is necessary for unit root tests to have good size, but that information criteria such as the AIC and the BIC tend to select a truncation lag (k) that is very small. We

We propose a new method for estimation in linear models. The "lasso" minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients

this article with the regression case. To explain this, we will introduce a suitable definition of a margin that is maximized in both cases

in literature. Unfortunately, these techniques have problems when discovering processes with complicated dependencies. This paper proposes a new two-step approach. First, a transition system is constructed which can be modified to avoid over-fitting. Then, using the “theory of regions”, the model is synthesized

For a general class of order selection criteria, we establish analytic and nonasymptotic evaluations of both the underfitting and overfitting sets of selected models. These evaluations are further specified in various situations including regressions and autoregressions with finite or infinite

accuracy and that, as a research community, we are making progress toward developing powerful, general methods for guarding against overfitting in inducing decision trees. In fact, any overfitting avoidance strategy amounts to a form of bias and, as such, may degrade performance instead of improving it

Abstract. Process mining techniques attempt to extract non-trivial and useful information from event logs. One aspect of process mining is control-flow discovery, i.e., automatically constructing a process model (e.g., a Petri net) describing the causal dependencies between activities. One