Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A Mixed Clustering Approach for Real Time Anomaly Detection

Version 1 : Received: 1 March 2023 / Approved: 2 March 2023 / Online: 2 March 2023 (04:15:10 CET)

A peer-reviewed article of this Preprint also exists.

Mazarbhuiya, F.A.; Shenify, M. A Mixed Clustering Approach for Real-Time Anomaly Detection. Appl. Sci. 2023, 13, 4151. Mazarbhuiya, F.A.; Shenify, M. A Mixed Clustering Approach for Real-Time Anomaly Detection. Appl. Sci. 2023, 13, 4151.


Anomaly Detection in real time data is accepted as a vital research area. Clustering has effectively been tried for this purpose. As the datasets are real time, the time of generating of the data is also important. In this article, we introduce a mixture of partitioning and agglomerative hierarchical approach to detect anomalies from such datasets. It is a two-phase method which follows partitioning approach first and then agglomerative hierarchical approach. The dataset can have mixed attributes. In phase-1, a unified metric defined on mixed attributes is used. The same is also used for merging of similar clusters in phase-2. Also, we have kept the track of time attribute of each data instance which produces the clusters with their lifetimes in phase-1. Then in phase-2, we merge the similar clusters. While merging, the similar clusters, the lifetimes of the corresponding clusters with overlapping cores are to be superimposed producing fuzzy time intervals. This way, each cluster will have an associated fuzzy lifetime. The data instances either belonging sparse clusters or not belonging to any of the clusters can be treated as anomalies. The efficacy of the algorithms can be established using both complexity analysis as well as experimental studies.


data instances; real time systems; k-means algorithm; agglomerative hierarchical algorithm; similarity measure; merge function 


Computer Science and Mathematics, Data Structures, Algorithms and Complexity

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