title: Exploring Dance Movement Data Using Sequence Alignment Methods.
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.
authors.affiliation: Department of Geography, Ghent University, Ghent, Belgium.
authors.forename: Seyed Hossein
authors.initials: SH
authors.lastname: Chavoshi
authors.affiliation: KERMIT, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Ghent, Belgium.
authors.forename: Bernard
authors.initials: B
authors.lastname: De Baets
authors.affiliation: Department of Geography, Ghent University, Ghent, Belgium.
authors.forename: Tijs
authors.initials: T
authors.lastname: Neutens
authors.affiliation: Department of Telecommunications and Information Processing, Ghent University, Ghent, Belgium.
authors.forename: Guy
authors.initials: G
authors.lastname: De Tré
authors.affiliation: Department of Geography, Ghent University, Ghent, Belgium.
authors.forename: Nico
authors.initials: N
authors.lastname: Van de Weghe
mesh_terms: D000465:Algorithms; D001696:Biomechanical Phenomena; D016000:Cluster Analysis; D003615:Dancing / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D009068:Movement; D016415:Sequence Alignment; D012984:Software