Research on spatial data mining based on knowledge discovery

Authors:
Zhong Qu;Lian Wang
Affiliations:
College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China;College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Year:
2006

Citing 5
Cited 0

GeoMiner: a system prototype for spatial data mining

SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Fuzzy Relative Position Between Objects in Image Processing: A Morphological Approach

IEEE Transactions on Pattern Analysis and Machine Intelligence
An Updated Bibliography of Temporal, Spatial, and Spatio-temporal Data Mining Research

TSDM '00 Proceedings of the First International Workshop on Temporal, Spatial, and Spatio-Temporal Data Mining-Revised Papers
Discovery of Spatial Association Rules in Geographic Information Databases

SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Discovering Association Rules Based on Image Content

ADL '99 Proceedings of the IEEE Forum on Research and Technology Advances in Digital Libraries

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes spatial outliers detection method of studying multiple non-spatial attributes based on special objects. The spatial outliers detection algorithm based on the Mahalanobis distance is proposed in this paper. The simulated experiment results demonstrate this method is feasible and effective, simultaneously the time complexity of center algorithm is analysed. Except the research type mentioned, spatial outliers detection still includes time-order data and time-space data outliers detections, which are related to the attribute values of other neighbors.