28 research outputs found
Warped K-Means: An algorithm to cluster sequentially-distributed data
[EN] Many devices generate large amounts of data that follow some sort of sequentiality, e.g.,
motion sensors, e-pens, eye trackers, etc. and often these data need to be compressed for
classification, storage, and/or retrieval tasks. Traditional clustering algorithms can be used
for this purpose, but unfortunately they do not cope with the sequential information
implicitly embedded in such data. Thus, we revisit the well-known K-means algorithm
and provide a general method to properly cluster sequentially-distributed data. We present
Warped K-Means (WKM), a multi-purpose partitional clustering procedure that minimizes
the sum of squared error criterion, while imposing a hard sequentiality constraint in the
classification step. We illustrate the properties of WKM in three applications, one being
the segmentation and classification of human activity. WKM outperformed five state-of-
the-art clustering techniques to simplify data trajectories, achieving a recognition accuracy
of near 97%, which is an improvement of around 66% over their peers. Moreover, such an
improvement came with a reduction in the computational cost of more than one order of
magnitude.This work has been partially supported by Casmacat (FP7-ICT-2011-7, Project 287576), tranScriptorium (FP7-ICT-2011-9, Project 600707), STraDA (MINECO, TIN2012-37475-0O2-01), and ALMPR (GVA, Prometeo/20091014) projects.Leiva Torres, LA.; Vidal, E. (2013). Warped K-Means: An algorithm to cluster sequentially-distributed data. Information Sciences. 237:196-210. https://doi.org/10.1016/j.ins.2013.02.042S19621023
Identifying Prototypical Components in Behaviour Using Clustering Algorithms
Quantitative analysis of animal behaviour is a requirement to understand the task solving strategies of animals and the underlying control mechanisms. The identification of repeatedly occurring behavioural components is thereby a key element of a structured quantitative description. However, the complexity of most behaviours makes the identification of such behavioural components a challenging problem. We propose an automatic and objective approach for determining and evaluating prototypical behavioural components. Behavioural prototypes are identified using clustering algorithms and finally evaluated with respect to their ability to represent the whole behavioural data set. The prototypes allow for a meaningful segmentation of behavioural sequences. We applied our clustering approach to identify prototypical movements of the head of blowflies during cruising flight. The results confirm the previously established saccadic gaze strategy by the set of prototypes being divided into either predominantly translational or rotational movements, respectively. The prototypes reveal additional details about the saccadic and intersaccadic flight sections that could not be unravelled so far. Successful application of the proposed approach to behavioural data shows its ability to automatically identify prototypical behavioural components within a large and noisy database and to evaluate these with respect to their quality and stability. Hence, this approach might be applied to a broad range of behavioural and neural data obtained from different animals and in different contexts