SeqClu-PV: An extension of online K-medoids to efficiently cluster sequences real-time

Bachelor thesis (2021)

Authors

R.E.C. te Wierik Electrical Engineering, Mathematics and Computer Science

Contributors

A. Nadeem Cyber Security - EEMCS (mentor)
S.E. Verwer Cyber Security - EEMCS (graduation committee member)
M.A. Migut Computer Science & Engineering-Teaching Team - EEMCS (coach)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2021 Ruben te Wierik
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Published Date

01-07-2021

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Real-time sequence clustering is the problem of clustering an infinite stream of sequences in real time with limited memory. A variant of the k-medoids algorithm called SeqClu is the suggested approach, representing a cluster with p most representative sequences of the cluster, called prototypes, to solve the problem of maintaining a high-quality representation of a cluster that requires little memory throughout time. However, the computational cost of this algorithm is considerable due to many distance computations that use Dynamic Time Warping (DTW), which is a computationally expensive distance measure that can be applied to sequences and is proven to be robust to noise and
delays. Therefore, this paper proposes an extension of SeqClu called SeqClu-PV, characterised by a decision-making mechanism for updating prototypes that improves the balance between the number of distance computations and the cost incurred due to incorrect clustering and reviews its performance.