Encoding time series data for better clustering results
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F13%3A00197065" target="_blank" >RIV/68407700:21240/13:00197065 - isvavai.cz</a>
Result on the web
<a href="http://link.springer.com/chapter/10.1007/978-3-642-33018-6_48" target="_blank" >http://link.springer.com/chapter/10.1007/978-3-642-33018-6_48</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-642-33018-6_48" target="_blank" >10.1007/978-3-642-33018-6_48</a>
Alternative languages
Result language
angličtina
Original language name
Encoding time series data for better clustering results
Original language description
Clustering algorithms belong to a category of unsupervised learning methods which aim to discover underlying structure in a dataset without given labels. We carry out research of methods for an analysis of a biological time series signals, putting stresson global patterns found in samples. When clustering raw time series data, high dimensionality of input vectors, correlation of inputs, shift or scaling sensitivity often deteriorates the result. In this paper, we propose to represent time series signals by various parametric models. A significant parameters are determined by means of heuristic methods and selected parameters are used for clustering. We applied this method to the data of cell's impedance profiles. Clustering results are more stable, accurate and computationally less expensive than processing raw time series data.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
INTERNATIONAL JOINT CONFERENCE CISIS'12 - ICEUTE'12 - SOCO'12 SPECIAL SESSIONS
ISBN
978-3-642-33017-9
ISSN
2194-5357
e-ISSN
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Number of pages
9
Pages from-to
467-475
Publisher name
Springer
Place of publication
Berlin
Event location
Ostrava
Event date
Sep 5, 2012
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
000312969500048