Efficient Trajectory Pattern Mining for Both Sparse and Dense Dataset

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Provided by: International Journal of Computer Applications
Topic: Data Management
Format: PDF
The comprehension of phenomena related to movement - not only of people and vehicles but also of animals and other moving objects - has always been a key issue in many areas of scientific investigation or social analysis. In this paper, the authors have examined ways of partitioning data for trajectory pattern discovery. Their aim has been to identify methods that will enable efficient counting of frequent sets in cases where the data is much too large to be contained in primary memory, and also where the density of the data means that the number of candidates to be considered becomes very large. Their starting point was a method which makes use of an initial preprocessing of the data into a tree structure (the P-tree) which incorporates a partial counting of support totals.
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