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Title	Study and implementation of clustering algorithms in time series. Application and testing of methods on economic data.
Author	Λευκαδίτη, Αικατερίνη
Thesis advisor	Πανταζής, Ιωάννης
Abstract	The spread of devices that produce large amounts of data requires new improved clustering algorithms by Computer Science. This data needs to be organized into compact structures, so that it is easy to use and requires less storage space. An approach to the solution of this classification and optimization problem is the Warped K-Means (WKM) method, a clustering algorithm of sequentially-distributed data, which is based on the well-known K-Means Algorithm and is focused on solving its sequential originated problem. The main objective of this project is to extend WKM, in order to include piecewise linear functions as clusters.
Language	English
Issue date	2021-11-26
Collection	School/Department--School of Sciences and Engineering--Department of Applied Mathematics--Graduate theses
	Type of Work--Graduate theses
Permanent Link	https://elocus.lib.uoc.gr//dlib/a/3/e/metadata-dlib-1641902549-979585-25891.tkl
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