@article{MAKHILLAJIT20121135703,
    title = {STEM: A Novel Approach for Spatiotemporal Sequence Mining},
    journal = {Asian Journal of Information Technology},
    volume = {11},
    number = {3},
    pages = {94-99},
    year = {2012},
    issn = {1682-3915},
    doi = {ajit.2012.94.99},
    url = {https://makhillpublications.co/view-article.php?issn=1682-3915&doi=ajit.2012.94.99},
    author = {Kelvin and},
    keywords = {Skelton,GSP,STEM,attributes,analysis},
    abstract = {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.}
    }