Time series clustering in large data sets

Jiří Fejfar, Jiří Šťastný
2011 Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis  
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. A er the fi rst 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 fi nd the
more » ... ion between confi gured parameters and results more precisely. The second requirement arose in a need for a well-defi ned 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 diff erent 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 diff erent topologies varying from few neurons to large maps. There are other algorithms discussed, usable for fi nding similarities between time series and fi nally conclusions for further research are presented. We also present an overview of the related actual literature and projects.
doi:10.11118/actaun201159020075 fatcat:4jssq4ii5bhy7bksjahpiqxm2a