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AN ALGORITHM ABOUT ASSOCIATION RULE MINING BASED ON SPATIAL 
AUTOCORRELATION 
Jiangping Chen a,b 
a School of remote sensing Information Engineering Wuhan University, Wuhan Hubei, PR China 430079 
chenjp_lisa@l 63.com, ch_lisa@hotmail.com 
b Department of Geography, University of Cambridge,Downing Place, Cambridge, UK, CB2 3EN 
jc564@cam.ac.uk 
KEY WORDS: Geographical information science, Statistical analysis, Data mining, Geography, Spatial association rule, spatial 
auto-correlation 
ABSTRACT: 
Most spatial data in GIS are not independent, they have high autocorrelation. For example, temperatures of nearby locations are often 
related. Most of the spatial association rule mining algorithms derived from the attribute association rule mining algorithms which assume 
that spatial data is independent. In these situations, the rules or knowledge derived from spatial mining will be wrong. It is, therefore, 
important that mining spatial association rules take into consideration spatial autocorrelation. At present, spatial statistics are the most 
common method to research spatial autocorrelation. In spatial statistics, classic statistics are extended by taking into account spatial 
autocorrelation. Spatial Autoregressive Model, SAR, is one of the methods; the adjacency matrix is used to describe the interaction of 
neighbouring fields which can simulate the effect of dependence between variables. The disadvantage of spatial statistics is that the 
calculation consuming is high so it cannot be widely applied in spatial data mining. 
This paper puts forward a new method of mining spatial association rules based on taking account of the spatial autocorrelation with 
an cell structure theory. It defines spatial data with an algebra data structure then the autocorrelation of the spatial data can be calculated 
in algebra. According to J. Corbett’s cell structure theory (1985), spatial graph is a subset of point, line, face, and body. The algebra 
structure of point, line, face and body can be used to express spatial data. In spatial data mining, we mine rules in the spatial database. 
In this paper the first step is to design a structure about point, line, face and body to express the spatial data and then store it in the 
spatial database. The second step is to build the measurement model of spatial autocorrelation based on the algebra structure of spatial 
data. The third step to mine the association rules based on the spatial autocorrelation model. Taking account of spatial autocorrelation is a 
significance research field for mining spatial association rules. We can get the spatial frequency items from the autocorrelation of the 
spatial data. This replaces the repeated scanning of the database by the measure of the spatial autocorrelation. 
computer cartography, environmental assessment and planning. 
The collected data far exceeds people's ability to analyze it. Thus, 
new and efficient methods are needed to discover knowledge 
from large spatial databases. Attribute data mining methods were 
extended to applying in spatial data mining.One of the big 
INTRODUCTION 
Spatial data mining, i.e, mining knowledge from large amounts of 
spatial data, is a demanding field since huge amounts of spatial 
data have been collected in various applications, ranging from 
remote sensing to geographical information systems (GIS),