Top-k distance-based outlier detection on uncertain data

Abstract

In recent years, more researchers are studying uncertain data with the development of Internet of Things. The technique of outlier detection is one of the significant branches of emerging uncertain database. In existing algorithms, parameters are difficult to set, and expansibility is poor when used in large data sets. Aimed at these shortcomings, a top-k distance-based outlier detection algorithm on uncertain data is proposed. This algorithm applies dynamic programming theory to calculate outlier possibility and greatly improves the efficiency. Furthermore, an efficient virtual grid-based optimization approach is also proposed to greatly improve our algorithm’s efficiency. The theoretical analysis and experimental results fully prove that the algorithm is feasible and efficient.