Outlier Detection Using a Novel method: Quantum Clustering

TL;DR

This paper introduces Quantum Clustering, a novel density-based method for unsupervised outlier detection that effectively identifies both obvious and subtle outliers across different datasets.

Contribution

The paper proposes a new assumption about data density fluctuations and develops Quantum Clustering to detect outliers without labeled data.

Findings

Quantum Clustering effectively finds hidden outliers.

Adjusting parameter σ reveals more subtle outliers.

Method demonstrates wide applicability across datasets.

Abstract

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 $\sigma$. 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.