International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B2. Istanbul 2004
in training spatial data sets. If [/, u] is divided into N fuzzy
areas A; (i =1,---n) and fuzzy area is represented by Gaussian
membership function, A; is defined as:
nai
HA Gc o Vx e [/.u] (21)
1 = ;
i-D(u- f -1l
Where c; E10. Du n g; ss,
N -1 2(N - INIn4
CE, te, +1 c; Serm
Ha (c; ) 1 — and gui , 8e >
=0.5
For example, if the discussion field is [0,1], Figure 3 is an
instance that the discussion field is partitioned to three
Gaussian fuzzy subsets.
Degree of membership
x
Figure 3. Gaussian fuzzy subsets
5. CONCLUSION
[t is our mind in this research to achieve both of objectives.
Firstly, the quality of spatial knowledge discovery can be
improved by analyzing the uncertainties and its characteristics
in each phase of spatial knowledge discovery and finding
efficient method to reduce its uncertainties. Secondly, although
the uncertainties of spatial knowledge discovery cannot be
completely eliminated, the uncertainty of spatial knowledge
discovery results can be represented in order to make use of the
knowledge discovered in spatial knowledge discovery. In this
paper, we briefly analyze the uncertainties in spatial data and
spatial knowledge discovery. Then, the framework of spatial
knowledge discovery based on fuzzy evidence theory was
constructed. Further work aims at experimental study based
upper theories and methods, visualization of spatial knowledge
and uncertainty propagation law in spatial knowledge
discovery is also our interesting.
ACKNOWLEDGES
The work described in this paper was supported by the funds
from National Natural Science Foundation of China (Project
No.60275021).
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