TY - GEN
T1 - A try for handling uncertainties in spatial data mining
AU - Wang, Shuliang
AU - Chen, Guoqing
AU - Li, Deyi
AU - Li, Deren
AU - Yuan, Hanning
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2004.
PY - 2004
Y1 - 2004
N2 - Uncertainties pervade spatial data mining. This paper proposes a method of spatial data mining handling randomness and fuzziness simultaneously. First, the uncertainties in spatial data mining are presented via characteristics, spatial data, knowledge discovery and knowledge representation. Second, the aspects of the uncertainties in spatial data mining are briefed. They often appear simultaneously, but most of the existing methods cannot deal with spatial data mining with more than one uncertainty. Third, cloud model is presented to mine spatial data with both randomness and fuzziness. It may also act as an uncertainty transition between a qualitative concept and its quantitative data, which is the basis of spatial data mining in the contexts of uncertainties. Finally, a case study on landslide-monitoring data mining is given. The results show that the proposed method can well deal with randomness and fuzziness during the process of spatial data mining.
AB - Uncertainties pervade spatial data mining. This paper proposes a method of spatial data mining handling randomness and fuzziness simultaneously. First, the uncertainties in spatial data mining are presented via characteristics, spatial data, knowledge discovery and knowledge representation. Second, the aspects of the uncertainties in spatial data mining are briefed. They often appear simultaneously, but most of the existing methods cannot deal with spatial data mining with more than one uncertainty. Third, cloud model is presented to mine spatial data with both randomness and fuzziness. It may also act as an uncertainty transition between a qualitative concept and its quantitative data, which is the basis of spatial data mining in the contexts of uncertainties. Finally, a case study on landslide-monitoring data mining is given. The results show that the proposed method can well deal with randomness and fuzziness during the process of spatial data mining.
UR - http://www.scopus.com/inward/record.url?scp=84975489294&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-30134-9_69
DO - 10.1007/978-3-540-30134-9_69
M3 - Conference contribution
AN - SCOPUS:84975489294
SN - 9783540232056
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 513
EP - 520
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
A2 - Negoita, Mircea Gh.
A2 - Howlett, Robert J.
A2 - Jain, Lakhmi C.
PB - Springer Verlag
T2 - 8th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2004
Y2 - 20 September 2004 through 25 September 2004
ER -