Mining Spatial Association Rules with 9DLT String Representation
Date Issued
2005
Date
2005
Author(s)
Ko, Wei-Min
DOI
en-US
Abstract
Nowadays, there are the increasing numbers of images accumulated on the Internet. Spatial data mining play an important role of extracting implicit knowledge,
spatial relationships among objects and other interesting patterns stored in spatial databases. In this thesis, we focus on finding the association rules of spatial relations
among objects in an image. Previously, some scholars have proposed viewpoint mining and co-relation mining method based on the 2D string representation. However, the mining results may be too detailed and the relations between the objects are vague.
Therefore, we propose a novel algorithm, 9DLT-Miner, where every image is represented by the 9DLT representation. 9DLT-Miner adopts the concept of the Apriori algorithm as well as uses the anti-monotone and 9DLT pruning strategies. Our
proposed method consists of two phase. In the first phase, we find all frequent patterns of length one. In the second phase, we use the frequent k-patterns (k>=1) to generate all candidate (k+1)-patterns and then scan the databases to count the support and check if a pattern is frequent. Repeat the steps in phase 2 until no more frequent
patterns can be found. Experimental results show that 9DLT-Miner prunes a large number of impossible frequent candidates, and it’s more efficient and scalable.
Subjects
空間資料探勘
9DLT字串
空間關聯規則
spatial data mining
9DLT string
spatial association rules
Type
other
File(s)![Thumbnail Image]()
Loading...
Name
ntu-94-R92725030-1.pdf
Size
23.31 KB
Format
Adobe PDF
Checksum
(MD5):a2a26c40567e258f95f95cb10ee23370