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WIREs Data Mining Knowl Discov
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Segmentation techniques for the summarization of individual mobility data

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Segmentation techniques partition a sequence of data points into a series of disjoint subsequences—segments—based on some criteria. Depending on the context and the nature of data themselves, segments return an approximate representation. The final result is a summarized representation of the sequence. This intuitive mechanism has been extensively studied, for example, for the summarization of time series in order to preserve the ‘shape’ of the sequence while omitting irrelevant details. This survey focuses on the use of segmentation methods for extracting behavioral information from individual mobility data, in particular from spatial trajectories. Such information can then be given a compact representation in the form of summarized trajectories, e.g., semantic trajectories and symbolic trajectories. Two major streams of research are discussed, spanning computational geometry and data mining respectively, that are emblematic of the multiplicity of views. WIREs Data Mining Knowl Discov 2017, 7:e1214. doi: 10.1002/widm.1214

Segmentation of a spatial trajectory: the breakpoints along the input trajectory identify the begin/end of segments. Every segment corresponds to some property that holds for the whole duration of the segment.
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(a) Data‐centric view: spatial trajectories are stored and accessed through a Moving Object database; (b) knowledge discovery view: spatial trajectories are first summarized, next possibly stored in some database or manipulated through some other application.
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(a) The movement of a person tracked for a few days at high sampling rate (by courtesy of John Krumm). (b) The movement of an animal (roe deer) tracked for over 1 year at low and irregular sampling rate.
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(a) Spatial trajectory; (b) SeqScan clustering: clusters, excursion points, and transition points.
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Taxonomy refinement: pattern‐driven segmentation.
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Taxonomy refinement: attribute‐driven segmentation.
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General taxonomy for segmentation techniques.
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Technologies > Structure Discovery and Clustering
Algorithmic Development > Spatial and Temporal Data Mining
Fundamental Concepts of Data and Knowledge > Data Concepts

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