TL;DR
This paper reviews the evolution, current state, and future challenges of anomaly mining, focusing on point-cloud and graph-based methods across diverse application domains.
Contribution
It provides a comprehensive overview of anomaly mining techniques, research problems, recent trends, and open issues in the fields of point-cloud and graph-based anomaly detection.
Findings
Identifies key research problems in anomaly mining.
Highlights recent trends in point-cloud and graph-based methods.
Discusses future directions and open challenges.
Abstract
Anomaly mining is an important problem that finds numerous applications in various real world domains such as environmental monitoring, cybersecurity, finance, healthcare and medicine, to name a few. In this article, I focus on two areas, (1) point-cloud and (2) graph-based anomaly mining. I aim to present a broad view of each area, and discuss classes of main research problems, recent trends and future directions. I conclude with key take-aways and overarching open problems.
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