Interactive visual clustering of large collections of trajectories

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. Howeve...

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Bibliographic Details
Published in:2009 IEEE Symposium on Visual Analytics Science and Technology pp. 3 - 10
Main Authors: Andrienko, G., Andrienko, N., Rinzivillo, S., Nanni, M., Pedreschi, D., Giannotti, F.
Format: Conference Proceeding
Language:English
Japanese
Published: IEEE 01.10.2009
Subjects:
classification
clustering
Clustering algorithms
Clustering methods
Data visualization
Functional analysis
geovisualization
Humans
Information analysis
Information systems
Joining processes
movement data
Scalability
scalable visualization
Spatio-temporal data
Spatiotemporal phenomena
trajectories
ISBN:9781424452835, 142445283X
Online Access:Get full text
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Summary: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 spatio-temporal 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.
ISBN:9781424452835
142445283X
DOI:10.1109/VAST.2009.5332584