EdgeCluster: A Resource-Aware Evolving Clustering for Streaming Data

In this paper, we propose a novel evolving clustering algorithm for streaming data entitled EdgeCluster. The proposed algorithm is resource efficient, making it suitable for use at edge devices with limited storage and computational capacity. The EdgeCluster is capable of modeling and monitoring a s...

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Bibliographic Details
Published in2024 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS) pp. 1 - 10
Main Authors Angelova, Milena, Boeva, Veselka, Abghari, Shahrooz
Format Conference Proceeding
LanguageEnglish
Published IEEE 23.05.2024
Subjects
Benchmark testing
Clustering algorithms
Computational modeling
concept drift
data mining
Data models
evolving clustering
Heuristic algorithms
Shape
smart monitoring
Uncertainty
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Summary:In this paper, we propose a novel evolving clustering algorithm for streaming data entitled EdgeCluster. The proposed algorithm is resource efficient, making it suitable for use at edge devices with limited storage and computational capacity. The EdgeCluster is capable of modeling and monitoring a streaming data phenomenon and identifying outlying behavior. In parallel with the monitoring, the EdgeCluster algorithm dynamically maintains the set of clusters that models the phenomenon's normal behavioral scenarios by taking newly arrived data into account and updating the clustering model accordingly. The EdgeCluster algorithm is evaluated and benchmarked to another resource-aware stream clustering algorithm, EvolveCluster, in two experimental data scenarios using synthetic and real-world datasets.
ISSN:2473-4691
DOI:10.1109/EAIS58494.2024.10569997