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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
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