Interactive visual clustering of large collections of trajectories

Abstract

One of the most common operations in exploration and analysis of various kinds of data is clustering, i.e. discovery and interpretation of groups of objects having similar properties and/or behaviors. In clustering, objects are often treated as points in multi-dimensional space of properties. However, structurally complex objects, such as trajectories of moving entities and other kinds of spatiotemporal data, cannot be adequately represented in this manner. Such data require sophisticated and computationally intensive clustering algorithms, which are very hard to scale effectively to large datasets not fitting in the computer main memory. We propose an approach to extracting meaningful clusters from large databases by combining clustering and classification, which are driven by a human analyst through an interactive visual interface.

A visual analytics framework for spatio-temporal analysis and modelling

“…Garg et al (2010) describe a procedure in which clusters of documents are built by combining computational and interactive techniques, then a classifier for assigning documents to the clusters is automatically generated, and then the user refines and debugs the model. This is similar to what is suggested by Andrienko et al (2009) for analysis of a very large collection of trajectories: first, clusters of trajectories following similar routes are defined on the basis of a subset of trajectories, second, a classification model is built and interactively refined, and, third, the model is used to assign new trajectories to the clusters.…”

An Affordance-Based Framework for Human Computation and Human-Computer Collaboration

“…For some tasks such as image labeling [21,33,59,62,73,74], visual search [6,10], and query validation [48,80], the systems presented rely heavily on the users' visual perceptive abilities, with the machine serving only as a facilitator between the human and the data. For other tasks such as exploring high-dimensional datasets [68,83], classification [4,51], and dimension reduction [28,36], machine affordances (which will be discussed at length in Section 5) are combined with human visual processing to achieve superior results.…”

“…Another affordance presented by the human user is audiolinguistic ability; that is, our ability to process sound 3 and language 4 . Although separate from the visual affordances generally leveraged in Visual Analytics systems, we suggest that the interplay between visual and nonvisual human faculties is equally important in supporting analytical reasoning.…”

“…Instead, we can leverage the experience of the human analyst as part of the collaborative process. For example, we have seen domain expertise leveraged to help diagnose network faults [47], classify MRI data [12], perform domain-specific data transformations [40], and navigation and infer trends about a specific geographic region [4].…”

“…The study of general human-computer collaboration offers a plethora of examples of successful human/machine teams [15,23,37,39,41,46,50,65,66,68,83]. Developments in supervised machine learning in the visualization community present several vetted techniques for human intervention into computationally complex tasks [3,4,12,18,28,29,36,47,51,57]. The emerging field of human computation inverts the traditional paradigm of machines providing computational support for problems that humans find challenging, and demonstrates success using aggregated human processing power facilitated by machines to perform difficult computational tasks such as image labeling [21,33,73,74], annotating audio clips [44,49], and even folding proteins [20].…”