A try for handling uncertainties in spatial data mining

Shuliang Wang, Guoqing Chen, Deyi Li, Deren Li, Manning Yuan

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)476-482
Number of pages7
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3215
Publication statusPublished - 2004
Externally publishedYes

Fingerprint

Dive into the research topics of 'A try for handling uncertainties in spatial data mining'. Together they form a unique fingerprint.

Cite this