NDLI: Discovering partial spatio-temporal co-occurrence patterns

Content Provider	IEEE Xplore Digital Library
Author	Celtic, M.
Copyright Year	2011
Description	Author affiliation: Dept. of Computer Engineering, Erciyes University 38039, Kayseri, Turkey (Celtic, M.)
Abstract	Spatio-temporal co-occurrence patterns represent subsets of object-types that are often located together in space and time. The aim of the discovery of partial spatio-temporal cooccurrence patterns (PACOPs) is to find co-occurrences of the object-types that are partially present in the database. Discovering PACOPs is an important problem with many applications such as discovering interactions between animals and identifying tactics in battlefields and games. However, mining PACOPs is computationally very expensive because the interest measures are computationally complex, databases are larger due to the archival history, and the set of candidate patterns is exponential in the number of object-types. Previous studies on discovering spatio-temporal co-occurrence patterns do not take into account the presence period (lifetime) of the objects in the database. In this paper, we define the problem of mining PACOPs, propose a new monotonic composite interest measure, and propose a novel PACOP mining algorithm. The experimental results show that the proposed algorithm is computationally more efficient than naïve alternatives.
Starting Page	116
Ending Page	120
File Size	132972
Page Count	5
File Format	PDF
ISBN	9781424483525
e-ISBN	9781424483518
DOI	10.1109/ICSDM.2011.5969016
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Composite Interest Measure Atmospheric measurements Spatio-temporal Data Mining Particle measurements Spatial Co-location Pattern Spatial databases Data mining Indexes Partial Spatio-temporal Co-occurrence Pattern Mining
Content Type	Text
Resource Type	Article

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