Outliers deviate from the norm—significantly enough to give marketers pause. But outliers can tell us more about our data, how we gather it, and what is in it, if we examine the entire data set ...

Outlier analyses are central to scientific data assessments. Conventional outlier identification methods do not work effectively for Protein Data Bank (PDB) data, which are characterized by heavy ...

Machine learning algorithms aren't just technological novelties relegated to tasks like picking out faces in crowded places. In the enterprise, they can surface patterns and relationships that would ...

Functional data analysis (FDA) encompasses a suite of statistical methods designed to handle data observed over a continuum such as time, space or wavelength, where each observation is naturally ...

The problem of identifying outliers has two important aspects: the choice of outlier measures and the method to assess the degree of outlyingness (norming) of those measures. Several classes of ...

After previously detailing how to examine data files and how to identify and deal with missing data, Dr. James McCaffrey of Microsoft Research now uses a full code sample and step-by-step directions ...