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Given a database of spatial trajectories reporting the movement of a set of objects in a time frame, the problem is to discover the groups of objects that stay in close proximity within a geographical area for a significant time. To deal with the problem, techniques for the discovery of collective patterns, e.g. the meeting pattern, have been proposed. Such techniques, however, impose stringent constraints...
In this paper, we propose a work flow for processing and analysing large-scale tracking data with spatio-temporal marks that uses an infrastructure for machine learning methods based on a meta-data representation of point patterns. The tracking log (IP address) of cyber security devices usually maps to geolocation and timestamp, such data is called spatiotemporal data. Existing spatio-temporal analysis...
Contrast patterns are itemsets that frequently occur in one dataset while not in another. These patterns have been successfully applied to many data mining domains, such as prediction, classification and clustering. However, none of the previous studies has considered extracting contrast patterns from different types of datasets. In this paper, we introduce a new type of contrast pattern, Conditional...
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