GUC: Unsupervised non-parametric Global Clustering and Anomaly Detection

Chris Solomou

Keywords: Clustering, Anomaly Detection, Big Data Optimization

TL;DR: We present a Novel Algorithm for efficient Global Clustering and Anomaly Detection

Abstract:

Anomaly Detection is a crucial task in the fields of optimization and Machine Learning,
with the ability of detecting global anomalies being of particular importance. In this paper,
we propose a novel non-parametric algorithm for automatically detecting global anomalies
in an unsupervised manner. Our algorithm is both effective and efficient, requiring no prior
assumptions or domain knowledge to be applied. It features two modes that utilize the
distance from the dataset’s center for grouping data points together. The first mode splits
the dataset into global clusters where each cluster signifies proximity from the center. The
second mode employs a threshold value for splitting the points into outliers and inliers. We
evaluate our proposal against other prominent methods using synthetic and real datasets.
Our experiments demonstrate that the proposed algorithm achieves state-of-the-art performance with minimum computational cost, and can successfully be applied to a wide range
of Machine Learning applications.

Submission Number: 4