FN Archimer Export Format
PT C
TI TSX-Means: An Optimal K Search Approach for Time Series Clustering
BT 
AF Tokotoko, Jannai
   Selmaoui-Folcher, Nazha
   Govan, Rodrigue
   Lemonnier, Hugues
AS 1:1;2:1;3:1;4:2;
FF 1:;2:;3:;4:PDG-RBE-LEADNC;
C1 ISEA, University of New Caledonia, Nouméa, New Caledonia
   UMR ENTROPIE, IFREMER, Nouméa, New Caledonia
C2 UNIV NOUVELLE CALEDONIE, FRANCE
   IFREMER, FRANCE
SI NOUMEA
SE PDG-RBE-LEADNC
UM ENTROPIE
IN WOS Ifremer UMR
   copubli-france
   copubli-univ-france
UR https://archimer.ifremer.fr/doc/00724/83583/89759.pdf
LA English
DT Proceedings paper
AB Proliferation of temporal data in many domains has generated considerable interest in the analysis and use of time series. In that context, clustering is one of the most popular data mining methods. Whilst time series clustering algorithms generally succeed in capturing differences in shapes, they most often fail to perform clustering based on both shape and amplitude dissimilarities. In this paper, we propose a new time series clustering method that automatically determines an optimal number of clusters. Cluster refinement is based on a new dispersion criterion applied to distances between time series and their representative within a cluster. That dispersion measure allows for considering both shape and amplitude of time series. We test our method on datasets and compare results with those from K-means time series (TSK-means) and K-shape methods. 
PY 2021
CT Strauss C., Kotsis G., Tjoa A.M., Khalil I. (eds) Database and Expert Systems Applications. 32nd International Conference, DEXA 2021, Virtual Event, September 27–30, 2021, Proceedings, Part II. Print ISBN 978-3-030-86474-3 Online ISBN 978-3-030-86475-0 . Lecture Notes in Computer Science, vol 12924, pp.232-238
DI 10.1007/978-3-030-86475-0_23
ID 83583
ER

EF