In this paper, we propose a new eccentricity- based anomaly detection principle and algorithm. It is based on a further development of the recently introduced data analytics framework (TEDA – from typicality and eccentricity data analytics). We compare TEDA with the traditional statistical approach and prove that TEDA is a generalization of it in regards to the well-known “ns” analysis (TEDA gives exactly the same result as the traditional “ns” analysis but it does not require the restrictive prior assumptions that are made for the traditional approach to be in place). Moreover, it offers a nonparametric, closed form analytical descriptions (models of the data distribution) to be extracted from the real data realizations,
not to be pre-assumed. In addition to that, for several types of proximity/similarity measures (such as Euclidean, cosine, Mahalonobis) it can be calculated recursively, thus, computationally very efficiently and is suitable for real time and online algorithms. Building on the per data sample, exact
information about the data distribution in a closed analytical form, in this paper we propose a new less conservative and more sensitive condition for anomaly detection. It is quite different from the traditional “ns” type conditions. We demonstrate example where traditional conditions would lead to an increased
amount of false negatives or false positives in comparison with the proposed condition. The new condition is intuitive and easy to check for arbitrary data distribution and arbitrary small (but not less than 3) amount of data samples/points. Finally, because the anomaly/novelty/change detection is very important and basic data analysis operation which is in the fundament of such
higher level tasks as fault detection, drift detection in data streams, clustering, outliers detection, autonomous video analytics, particle physics, etc. we point to some possible applications which will be the domain of future work.