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Mining Complex Spatial Patterns: Issues and Techniques

Samson, Grace, Lu, Joan and Showole, Aminat A. (2014) Mining Complex Spatial Patterns: Issues and Techniques. Journal of Information & Knowledge Management, 13 (2). p. 1450019. ISSN 0219-6492

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Abstract

Spatial data mining is the quantitative study of
phenomena that are located in space. This paper investigates
methods of mining patterns of a complex spatial data set (which
generally describes any kind of data where the location in space
of object holds importance). We based this research on the
analysis of some spatial characteristics of certain objects. We
began with describing the spatial pattern of events or objects
with respect to their attributes; we looked at how to describe the
spatial nature/characteristics of entities in an environment with
respect to their spatial and non-spatial attributes. We also
looked at modelling (predictive modelling/knowledge management
of complex spatial systems), querying and implementing a
complex spatial database (using data structure and algorithms).
Critically speaking, the presence of spatial auto-correlation and
the fact that continuous data types are always present in spatial
data makes it important to create methods, tools and algorithms
to mine spatial patterns in a complex spatial data set. This work
is particularly useful to researchers in the ¯eld of data mining as
it contributes a whole lot of knowledge to di®erent application
areas of data mining especially spatial data mining. It can also be
useful in teaching and likewise for other study purposes.

Item Type: Article
Contributors:
ContributionNameEmailORCID
AuthorSamson, Gracegracedyk@yahoo.comUNSPECIFIED
AuthorLu, Joanj.lu@hud.ac.ukUNSPECIFIED
AuthorShowole, Aminataminatshowole@gmail.comUNSPECIFIED
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Schools: School of Computing and Engineering
Related URLs:
Depositing User: Joan Lu
Date Deposited: 08 Jul 2014 10:41
Last Modified: 04 Nov 2015 20:49
URI: http://eprints.hud.ac.uk/id/eprint/20981

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