Outlier Detection Using a Novel method: Quantum Clustering
This work addresses outlier detection for unlabeled data in domains like environmental monitoring and historical analysis, but it appears incremental as it builds on existing density-based methods with a new twist.
The authors tackled outlier detection by proposing a novel density-based assumption and method called Quantum Clustering, which effectively identifies hidden outliers and allows for subtle detection through parameter adjustment, as demonstrated on datasets from air quality and historical correspondence.
We propose a new assumption in outlier detection: Normal data instances are commonly located in the area that there is hardly any fluctuation on data density, while outliers are often appeared in the area that there is violent fluctuation on data density. And based on this hypothesis, we apply a novel density-based approach to unsupervised outlier detection. This approach, called Quantum Clustering (QC), deals with unlabeled data processing and constructs a potential function to find the centroids of clusters and the outliers. The experiments show that the potential function could clearly find the hidden outliers in data points effectively. Besides, by using QC, we could find more subtle outliers by adjusting the parameter $σ$. Moreover, our approach is also evaluated on two datasets (Air Quality Detection and Darwin Correspondence Project) from two different research areas, and the results show the wide applicability of our method.