TY  - JOUR
T1  - STEM: A Novel Approach for Spatiotemporal Sequence Mining
AU - Leong, Kelvin AU - Chan, Stephen 
JO  - Asian Journal of Information Technology
VL  - 11
IS  - 3
SP  - 94
EP  - 99
PY  - 2012
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2012.94.99
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2012.94.99
KW  - Skelton
KW  -GSP
KW  -STEM
KW  -attributes
KW  -analysis
AB  - Building on the skeleton of Generalized Sequential Pattern 
  (GSP), researchers propose a new approach-Spatio-Temporal Events Miner (STEM) 
  for sequential pattern analysis. The STEM extends the traditional finding by 
  coverage of both temporal and spatial attributes. Furthermore, researchers are 
  interested in studying whether AprioriAll Method in traditional GSP is most 
  suitable in STEM.
ER  -