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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Vydáno v:2009 IEEE Symposium on Visual Analytics Science and Technology s. 3 - 10
Hlavní autoři: Andrienko, G., Andrienko, N., Rinzivillo, S., Nanni, M., Pedreschi, D., Giannotti, F.
Médium: Konferenční příspěvek
Jazyk:angličtina
japonština
Vydáno: IEEE 01.10.2009
Témata:
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
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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 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.
AbstractList 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.
Author Giannotti, F.
Rinzivillo, S.
Nanni, M.
Andrienko, G.
Pedreschi, D.
Andrienko, N.
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  organization: KDD Lab., CNR, Pisa, Italy
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  surname: Nanni
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  organization: KDD Lab., CNR, Pisa, Italy
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  surname: Pedreschi
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  organization: Univ. of Pisa, Pisa, Italy
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  givenname: F.
  surname: Giannotti
  fullname: Giannotti, F.
  organization: KDD Lab., CNR, Pisa, Italy
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Snippet 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...
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SubjectTerms 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
Title Interactive visual clustering of large collections of trajectories
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