Παρακαλώ χρησιμοποιήστε αυτό το αναγνωριστικό για να παραπέμψετε ή να δημιουργήσετε σύνδεσμο προς αυτό το τεκμήριο:
https://ruomo.lib.uom.gr/handle/7000/1191
Πλήρης εγγραφή μεταδεδομένων
Πεδίο DC | Τιμή | Γλώσσα |
---|---|---|
dc.contributor.author | Karamitopoulos, Leonidas | - |
dc.contributor.author | Evangelidis, Georgios | - |
dc.date.accessioned | 2022-08-26T11:04:05Z | - |
dc.date.available | 2022-08-26T11:04:05Z | - |
dc.date.issued | 2009 | - |
dc.identifier | 10.1109/BCI.2009.22 | en_US |
dc.identifier.isbn | 978-0-7695-3783-2 | en_US |
dc.identifier.uri | https://doi.org/10.1109/BCI.2009.22 | en_US |
dc.identifier.uri | https://ruomo.lib.uom.gr/handle/7000/1191 | - |
dc.description.abstract | In this paper, we present a new method that accelerates similarity search implemented via one-nearest neighbor on time series data. The main idea is to identify the most similar time series to a given query without necessarily searching over the whole database. Our method is based on partitioning the search space by applying the K-means algorithm on the data. Then, similarity search is performed hierarchically starting from the cluster that lies most closely to the query. This procedure aims at reaching the most similar series without searching all clusters. In this work, we propose to reduce the intrinsically high dimensionality of time series prior to clustering by applying a well known dimensionality reduction technique, namely, the piecewise aggregate approximation, for its simplicity and efficiency. Experiments are conducted on twelve real-world and synthetic datasets covering a wide range of applications. | en_US |
dc.language.iso | en | en_US |
dc.rights | Attribution-NonCommercial-ShareAlike 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | * |
dc.subject | FRASCATI::Natural sciences::Computer and information sciences | en_US |
dc.subject.other | similarity search | en_US |
dc.subject.other | clustering | en_US |
dc.subject.other | time series | en_US |
dc.subject.other | data mining | en_US |
dc.title | Cluster-Based Similarity Search in Time Series | en_US |
dc.type | Conference Paper | en_US |
dc.contributor.department | Τμήμα Εφαρμοσμένης Πληροφορικής | en_US |
local.identifier.firstpage | 113 | en_US |
local.identifier.lastpage | 118 | en_US |
local.identifier.volumetitle | 2009 Fourth Balkan Conference in Informatics | en_US |
Εμφανίζεται στις Συλλογές: | Τμήμα Εφαρμοσμένης Πληροφορικής |
Αρχεία σε αυτό το Τεκμήριο:
Αρχείο | Περιγραφή | Μέγεθος | Μορφότυπος | |
---|---|---|---|---|
2009_BCI.pdf | 204,97 kB | Adobe PDF | Προβολή/Ανοιγμα |
Αυτό το τεκμήριο προστατεύεται από Αδεια Creative Commons