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Exploring Dance Movement Data Using Sequence Alignment Methods.

Chavoshi SH, De Baets B, Neutens T, De Tré G, Van de Weghe N - PLoS ONE (2015)

Bottom Line: First, we transform movement data into sequences of successive qualitative relations based on the Qualitative Trajectory Calculus (QTC).Finally, movement sequences are grouped based on similarity by means of an agglomerative hierarchical clustering method.The applicability of this approach is tested using movement data from samba and tango dancers.

View Article: PubMed Central - PubMed

Affiliation: Department of Geography, Ghent University, Ghent, Belgium.

ABSTRACT
Despite the abundance of research on knowledge discovery from moving object databases, only a limited number of studies have examined the interaction between moving point objects in space over time. This paper describes a novel approach for measuring similarity in the interaction between moving objects. The proposed approach consists of three steps. First, we transform movement data into sequences of successive qualitative relations based on the Qualitative Trajectory Calculus (QTC). Second, sequence alignment methods are applied to measure the similarity between movement sequences. Finally, movement sequences are grouped based on similarity by means of an agglomerative hierarchical clustering method. The applicability of this approach is tested using movement data from samba and tango dancers.

No MeSH data available.


Dendrograms based on Fig 9.
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pone.0132452.g012: Dendrograms based on Fig 9.

Mentions: For example, the results of the clustering of QTCB movement sequences in samba case are represented in the form of dendrograms in Fig 12. A dendrogram supports the determination of a typology of different movement behaviours of dancers. The results of applying sequence alignment to real dance data suggest that certain movements were harder to follow by the students than other movements. Fig 12 shows the agglomerative hierarchical clustering in the form of dendrograms for the sequences as presented in Fig 9.


Exploring Dance Movement Data Using Sequence Alignment Methods.

Chavoshi SH, De Baets B, Neutens T, De Tré G, Van de Weghe N - PLoS ONE (2015)

Dendrograms based on Fig 9.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4504678&req=5

pone.0132452.g012: Dendrograms based on Fig 9.
Mentions: For example, the results of the clustering of QTCB movement sequences in samba case are represented in the form of dendrograms in Fig 12. A dendrogram supports the determination of a typology of different movement behaviours of dancers. The results of applying sequence alignment to real dance data suggest that certain movements were harder to follow by the students than other movements. Fig 12 shows the agglomerative hierarchical clustering in the form of dendrograms for the sequences as presented in Fig 9.

Bottom Line: First, we transform movement data into sequences of successive qualitative relations based on the Qualitative Trajectory Calculus (QTC).Finally, movement sequences are grouped based on similarity by means of an agglomerative hierarchical clustering method.The applicability of this approach is tested using movement data from samba and tango dancers.

View Article: PubMed Central - PubMed

Affiliation: Department of Geography, Ghent University, Ghent, Belgium.

ABSTRACT
Despite the abundance of research on knowledge discovery from moving object databases, only a limited number of studies have examined the interaction between moving point objects in space over time. This paper describes a novel approach for measuring similarity in the interaction between moving objects. The proposed approach consists of three steps. First, we transform movement data into sequences of successive qualitative relations based on the Qualitative Trajectory Calculus (QTC). Second, sequence alignment methods are applied to measure the similarity between movement sequences. Finally, movement sequences are grouped based on similarity by means of an agglomerative hierarchical clustering method. The applicability of this approach is tested using movement data from samba and tango dancers.

No MeSH data available.