An approach for mining complex spatial dataset

Grace Samson, Joan Lu, Lizen Wang, Dave Wilson

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Abstract: Spatial data mining organizes by location what
is interesting as such, specific features of spatial data
mining (including observations that are not independent
and spatial autocorrelation among the features) that
preclude the use of general purpose data mining
algorithms poses a serious challenge in the task of
mining meaningful patterns from spatial systems. This
creates the complexity that characterises complex spatial
systems. Thus, the major challenge for a spatial data
miner in trying to build a general complex spatial model
would be; to be able to integrate the elements of these
complex systems in a way that is optimally effective in
any particular case. We have examined ways of creating
explicit spatial model that represents an application of
mining techniques capable of analysing data from a
complex spatial system and then producing information
that would be useful in various disciplines where spatial
data form the basis of general interest
Original languageEnglish
Title of host publicationProceedings of the International Conference on Information and Knowledge Engineering
EditorsHamid R. Arabnia, Leonidas Deligiannidis, Ray Hashemi, Joan Lu
PublisherCSREA Press
Number of pages7
ISBN (Print)1601322518
Publication statusPublished - 2013
EventInternational Conference on Information and Knowledge Engineering - Las Vegas, United States
Duration: 22 Jul 201325 Jul 2013


ConferenceInternational Conference on Information and Knowledge Engineering
Abbreviated titleIKE'13
Country/TerritoryUnited States
CityLas Vegas
Internet address


Dive into the research topics of 'An approach for mining complex spatial dataset'. Together they form a unique fingerprint.

Cite this