Relative Density-Based Outlier Detection Algorithm
Jin Ning, Leiting Chen, Junwei Chen · Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence · 2018
Outlier detection is an important data mining technique to identify interesting and novel patterns, trends and anomalies from data. Density-based methods are among the most popular class of methods used in outlier detection. However, these methods suffer from the low density patterns problem that could lead to poor performance. In this paper, a novel relative density-based outlier detection algorithm is proposed, which utilizes a new measure of an object's neighborhood density. This approach takes into account an important factor for density: relative neighborhood. Experiments on both simulated and real data demonstrate that the proposed algorithm achieves better performance than other alternatives.