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  4. Mining spatial association rules in image databases
 
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Mining spatial association rules in image databases

Journal
Information Sciences
Journal Volume
177
Journal Issue
7
Pages
1593-1608
Date Issued
2007
Author(s)
Lee A.J.T.  
Hong R.-W.
Ko W.-M.
Tsao W.-K.
Lin H.-H.
DOI
10.1016/j.ins.2006.09.018
URI
https://scholars.lib.ntu.edu.tw/handle/123456789/415128
URL
https://www.scopus.com/inward/record.uri?eid=2-s2.0-33846247084&doi=10.1016%2fj.ins.2006.09.018&partnerID=40&md5=89a5f125b5ca3b7444609c32af864b4b
Abstract
In this paper, we propose a novel spatial mining algorithm, called 9DLT-Miner, to mine the spatial association rules from an image database, where every image is represented by the 9DLT representation. The proposed method consists of two phases. First, we find all frequent patterns of length one. Next, we use frequent k-patterns (k ? 1) to generate all candidate (k + 1)-patterns. For each candidate pattern generated, we scan the database to count the pattern's support and check if it is frequent. The steps in the second phase are repeated until no more frequent patterns can be found. Since our proposed algorithm prunes most of impossible candidates, it is more efficient than the Apriori algorithm. The experiment results show that 9DLT-Miner runs 2-5 times faster than the Apriori algorithm. ? 2006 Elsevier Inc. All rights reserved.
Subjects
9DLT string
Image database
Spatial association rule
Spatial data mining
Type
journal article

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