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

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

te Wierik, Ruben (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Intelligent Systems)

Nadeem, A. (mentor)
Verwer, S.E. (graduation committee)
Migut, M.A. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2021-07-01

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.

Clustering algorithms
k-medoids
Online
Approximation algorithms

http://resolver.tudelft.nl/uuid:4abdf786-d4cc-4fb1-b214-e75307e0ca92

https://github.com/rtewierik/seqclupv The link to the GitHub repository containing the code and research results generated during the research project. https://pypi.org/project/seqclupv The link to the publicly available Python package released via the Python Package Index (PyPI).

Student theses

bachelor thesis

© 2021 Ruben te Wierik