Databases Reference
In-Depth Information
3
Applications of Computational Geometry to GIS
3.1
Spatial Data Mining in Geographical Databases
Data Mining, or Knowledge Discovery in Databases (KDD) is to find interesting,
previously unknown and useful information from large databases. There have
been many studies of data mining in relational as well as transaction databases as
the first targets of this field. Now, data mining has been extended to other types of
databases such as data mining spatial databases, or, spatial data mining
4)
, and our
related results in this setting
6)
. Then, we discuss issues to investigate for data mining
in geographical databases, especially topological geographical data.
Data mining in spatial databases, or spatial data mining has been proposed; see
4)
. Spatial data mining refers to the extraction of implicit knowledge, spatial
relations, or other patterns not explicitly stores in spatial databases.
Figure 1: Application of the
k
-means algorithm for 20726 points at Kanto district in Japan
with
k
=100: (a) initial random solution, (b) solution obtained by the
k
-mean
In the existing spatial data mining, basically part of geographic information having
strong connection with remote sensing, image databases exploration seems to have
been investigated, and a clustering approach is adopted to derive knowledge. Main
algorithmic tools used in this approach are
k
-mean,
k
-medoid and their extensions.
The basic algorithms for them are well-known and have been used in many areas.
Especially, in connection with geographical databases, the so-called geographical
optimization approach provides a general algorithmic framework in terms of
mathematical programming and computational geometry
10)
. To give an idea about
these, we here show an example in Fig.1, taken from
6)
, of applying the
k
-mean
algorithm to about 20,000 points corresponding to big crossings of road network
in Kanto district in Japan. This clustering itself is basically intended for experimental
use, and not for some specific data mining, and yet this example would illustrate
how large the amount of geographical data even in this restricted area and its
