Mining spatial association rules in image databases
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. Ne...
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| Published in | Information sciences Vol. 177; no. 7; pp. 1593 - 1608 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Inc
01.04.2007
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0020-0255 1872-6291 |
| DOI | 10.1016/j.ins.2006.09.018 |
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| 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. |
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| AbstractList | 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. |
| Author | Tsao, Wen-Kwang Lin, Hsiu-Hui Lee, Anthony J.T. Ko, Wei-Min Hong, Ruey-Wen |
| Author_xml | – sequence: 1 givenname: Anthony J.T. surname: Lee fullname: Lee, Anthony J.T. email: jtlee@ntu.edu.tw – sequence: 2 givenname: Ruey-Wen surname: Hong fullname: Hong, Ruey-Wen email: d90004@im.ntu.edu.tw – sequence: 3 givenname: Wei-Min surname: Ko fullname: Ko, Wei-Min email: r92030@im.ntu.edu.tw – sequence: 4 givenname: Wen-Kwang surname: Tsao fullname: Tsao, Wen-Kwang email: d93725001@ntu.edu.tw – sequence: 5 givenname: Hsiu-Hui surname: Lin fullname: Lin, Hsiu-Hui email: d8725003@im.ntu.edu.tw |
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| Keywords | Spatial data mining 9DLT string Image database Spatial association rule |
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| Title | Mining spatial association rules in image databases |
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