Mining Spatial Association Rules with 9D-SPA Representation
Date Issued
2007
Date
2007
Author(s)
DOI
en-US
Abstract
In this thesis, we propose a novel spatial data mining algorithm, called 9DSPA-Miner, to mine the spatial association rules from an image database, where every image is represented by the 9D-SPA representation. Our proposed method consists of three phases. In the first phase, we scan the database once and create an index structure. In the second phase, we scan the index structure to find all frequent patterns of length two. In the third phase, we use the frequent k-patterns (k≧2) to generate candidate (k+1)-patterns and check each generated candidate if its support is not less than the user-specified minimum support threshold by using the index structure. Then, the steps in phase 3 are repeated until no more frequent patterns can be found. Since 9DSPA-Miner uses the characteristics of the 9D-SPA representation to prune most of impossible candidates and the index structure to speed up the mining process, the experiment results demonstrate that it is more efficient and scalable than the modified Apriori method.
Subjects
空間資料探勘
空間關聯規則
9D-SPA表示法
spatial data mining
spatial association rules
9D-SPA representation
Type
other
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