An eigenvector-based hotspot detection

TL;DR

This paper introduces SST-Hotspot, an algorithm that uses tensor decomposition and eigenvector techniques to detect spatiotemporal hotspots by analyzing variations in data, demonstrating promising results in anomaly detection.

Contribution

The paper presents a novel tensor-based eigenvector approach for spatiotemporal hotspot detection, advancing methods in anomaly analysis in complex data.

Findings

Effective detection of hotspots using eigenvector matching

Tensor decomposition enhances spatiotemporal analysis

Experimental results show promising application in anomaly detection

Abstract

Space and time are two critical components of many real world systems. For this reason, analysis of anomalies in spatiotemporal data has been a great of interest. In this work, application of tensor decomposition and eigenspace techniques on spatiotemporal hotspot detection is investigated. An algorithm called SST-Hotspot is proposed which accounts for spatiotemporal variations in data and detect hotspots using matching of eigenvector elements of two cases and population tensors. The experimental results reveal the interesting application of tensor decomposition and eigenvector-based techniques in hotspot analysis.