Print Email Facebook Twitter Cold start is coming: How to approximate the optimal set of initial prototypes for clustering sequence data online Title Cold start is coming: How to approximate the optimal set of initial prototypes for clustering sequence data online Author Fucarev, Silviu (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Intelligent Systems) Contributor Nadeem, A. (mentor) Verwer, S.E. (mentor) Migut, M.A. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2021-07-01 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. Subject Clustering algorithmsgreedy heuristick-medoidsonline clustering algorithms To reference this document use: http://resolver.tudelft.nl/uuid:59e50492-e027-4f04-9d86-f8c659851cc6 Part of collection Student theses Document type bachelor thesis Rights © 2021 Silviu Fucarev Files PDF thesis.pdf 2 MB Close viewer /islandora/object/uuid:59e50492-e027-4f04-9d86-f8c659851cc6/datastream/OBJ/view