Spatial entropy-based clustering for mining data with spatial correlation

Authors:
Baijie Wang;Xin Wang
Affiliations:
Department of Geomatics Engineering, University of Calgary, Calgary, AB, Canada;Department of Geomatics Engineering, University of Calgary, Calgary, AB, Canada
Venue:
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Year:
2011

Citing 4
Cited 0

Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications

Data Mining and Knowledge Discovery
CLARANS: A Method for Clustering Objects for Spatial Data Mining

IEEE Transactions on Knowledge and Data Engineering
DBRS: a density-based spatial clustering method with random sampling

PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
A spatial form of diversity

COSIT'05 Proceedings of the 2005 international conference on Spatial Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

Due to the inherent characteristics of spatial datasets, spatial clustering methods need to consider spatial attributes, nonspatial attributes and spatial correlation among non-spatial attributes across space. However, most existing spatial clustering methods ignore spatial correlation, considering spatial and non-spatial attributes independently. In this paper, we first prove that spatial entropy is a monotonic decreasing function for non-spatial attribute similarity and spatial correlation. Then we propose a novel density-based spatial clustering method called SEClu, which applies spatial entropy in measuring non-spatial attribute similarity and spatial correlation during the clustering process. The experimental results from both the synthetic data and the real application demonstrate that SEClu can effectively identify spatial clusters with spatial correlated patterns.