Visually driven analysis of movement data by progressive clustering

Abstract

This is the unspecified version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractThe paper investigates the possibilities of using clustering techniques in visual exploration and analysis of large numbers of trajectories, i.e. sequences of time-stamped locations of some moving entities. Trajectories are complex spatio-temporal constructs characterized by diverse non-trivial properties. To assess the degree of (dis)similarity between trajectories, specific methods (distance functions) are required. A single distance function accounting for all properties of trajectories, first, is difficult to build, second, would require much time to compute, third, might be difficult to understand and to use. We suggest the procedure of progressive clustering where a simple distance function with a clear meaning is applied on each step, which leads to easily interpretable outcomes. Successive application of several different functions enables sophisticated analyses through gradual refinement of earlier obtained results. Besides the advantages from the sense-making perspective, progressive clustering enables a rational work organization where time-consuming computations are applied to relatively small potentially interesting subsets obtained by means of "cheap" distance functions producing quick results. We introduce the concept of progressive clustering by an example of analyzing a large real dataset. We also review the existing clustering methods, describe the method OPTICS suitable for progressive clustering of trajectories, and briefly present several distance functions for trajectories.

On Computationally-Enhanced Visual Analysis of Heterogeneous Data and Its Application in Biomedical Informatics

“…Kandogan [71] discusses how clusters can be found and annotated through an image-based technique. Rinzivillo et al [72] use a visual technique called progressive clustering where the clustering is done using different distance functions in consecutive steps. Schreck et al [73] propose a framework to interactively monitor and control Kohonen maps to cluster trajectory data.…”

“…[51], [52], [53], [54], [55], [56] Groups & Classification [57] [58], [59] [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74] [75], [76], [77], [78], [79], [80] Dependence & Prediction [81], [82], [46] [83], [84], [85], [86], [87], [88], [89] [90], [91], [92] being analyzed. The results are then presented to the user through different visual encodings that are often accompanied by interaction.…”

A visual analytics framework for spatio-temporal analysis and modelling

“…If some cluster has high internal variability, it should be subdivided into smaller clusters. We do this by means of progressive clustering (Rinzivillo et al 2008), i.e., applying the clustering tool to members of one or a few chosen clusters. This is illustrated in Figure 6, where the time series are shown on a time graph display in a summarised way, as described in ( Figure 6A summarises the 32 time series of cluster 6.…”

Analyzing Trajectories Using Uncertainty and Background Information

“…Nanni and Pedreschi [15] generalize the spatial notion of distance between objects to a spatio-temporal notion of distance between trajectories, leading to a natural extension of the density-based clustering technique to trajectories. More recently, the concept of progressive clustering has been introduced, as a process that, using a visual analytics approach, starts from the simpler functions to complex ones, in an incremental way [18], using an iterative approach that filters clusters using simpler but efficient distance functions in the firsts steps.…”

“…However, depending on the application at hand, or the analysis a user needs to perform, other forms of distances could be useful. For instance, we may want to cluster together trajectories starting and ending at the same locations [18]. Nevertheless, as far as we are aware of, no distance function has been proposed to account for an intrinsic problem trajectory data have, that is, the uncertainty involved in GPS or GSM observations that originate the trajectory samples.…”