Publication detail

Time series clustering in large data sets

FEJFAR, J. ŠŤASTNÝ, J.

Original Title

Time series clustering in large data sets

Type

journal article - other

Language

English

Original Abstract

The clustering of time series is a widely researched area. There are many methods for dealing with this task. We are actually using the Self-organizing map (SOM) with the unsupervised learning algorithm for clustering of time series. After the first experiment (Fejfar, Weinlichová, Šťastný, 2009) it seems that the whole concept of the clustering algorithm is correct but that we have to perform time series clustering on much larger dataset to obtain more accurate results and to find the correlation between configured parameters and results more precisely. The second requirement arose in a need for a well-defined evaluation of results. It seems useful to use sound recordings as instances of time series again. There are many recordings to use in digital libraries, many interesting features and patterns can be found in this area. We are searching for recordings with the similar development of information density in this experiment. It can be used for musical form investigation, cover songs detection and many others applications. The objective of the presented paper is to compare clustering results made with different parameters of feature vectors and the SOM itself. We are describing time series in a simplistic way evaluating standard deviations for separated parts of recordings. The resulting feature vectors are clustered with the SOM in batch training mode with different topologies varying from few neurons to large maps. There are other algorithms discussed, usable for finding similarities between time series and finally conclusions for further research are presented. We also present an overview of the related actual literature and projects.

Keywords

clustering, Self-organizing map

Authors

FEJFAR, J.; ŠŤASTNÝ, J.

RIV year

2011

Released

1. 1. 2011

ISBN

1211-8516

Periodical

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis

Year of study

2011

Number

2

State

Czech Republic

Pages from

75

Pages to

80

Pages count

6

BibTex

@article{BUT74807,
  author="Jiří {Fejfar} and Jiří {Šťastný}",
  title="Time series clustering in large data sets",
  journal="Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis",
  year="2011",
  volume="2011",
  number="2",
  pages="75--80",
  issn="1211-8516"
}