Outlier detection aims at searching for a small set of objects that are inconsistent or considerably deviating from other objects in a dataset. Existing research focuses on outlier identification while omitting the equally important problem of outlier interpretation. This paper presents a novel method named LODI to address both problems at the same time. In LODI, we develop an approach that exp...

Outlier detection has recently become an important problem in many industrial and financial applications. In this paper, a novel unsupervised algorithm for outlier detection with a solid statistical foundation is proposed. First we modify a nonparametric density estimate with a variable kernel to yield a robust local density estimation. Outliers are then detected by comparing the local density ...

Of all the data mining techniques, outlier detection seems closest to the definition of “discovering nuggets of information” in large databases. When an outlier is detected, and determined to be genuine, it can provide insights, which can radically change our understanding of the underlying process. The purpose of the research underlying this thesis was to investigate and devise methods to mine...