Cold start is coming: How to approximate the optimal set of initial prototypes for clustering sequence data online

Bachelor thesis (2021)

Authors

S. Fucarev Electrical Engineering, Mathematics and Computer Science

Contributors

A. Nadeem Cyber Security - (mentor)

S.E. Verwer Cyber Security - (mentor)

M.A. Migut Computer Science & Engineering-Teaching Team - (graduation committee member)

Faculty

Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science

Clustering algorithms Greedy heuristic K-medoids Online clustering algorithms

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Published Date

01-07-2021

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Clustering data is a classic topic in the academic community and in the industry. It is by and large one of the most popular unsupervised classification techniques. It is fast and flexible as it can accommodate all kinds of data when a suitable similarity metric is found. SeqClu is an online k-medoids prototype based clustering algorithm designed to handle large quantities of sequence data. Our main focus is the role initialization plays in the performance of SeqClu. In this paper we show that Greedy Heuristics perform significantly better than K-medoids heuristics. In the context of Greedy Heuristics we show that these can be combined together to achieve potentially better accuracy if a proper metric to choose the initialization results is elected.

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