Mining Closed Patterns in 9DLT Image Databases
Date Issued
2008
Date
2008
Author(s)
Wang, Wei-Ting
Abstract
With advances of information technology, enormous numbers of images have been accumulated in image databases. As a result, how to mine useful patterns from image databases has attracted more and more attention in recent years. Hence, in this thesis, we proposed an efficient and scalable algorithm, CP9, to mine the frequent closed pattern in a 9DLT database, where every image is represented by a 9DLT string. Our proposed algorithm consists of two phases. First, we scan the database to find all frequent 2-patterns, and build an imageset for each frequent 2-pattern. Then, we use a frequent k-pattern to find its super (k+1)–patterns by joining the patterns in its joinable class in a depth-first search (DFS) manner where k>=2. The second phase is recursively repeated until no more frequent closed patterns can be found. Since our proposed algorithm uses the joinable class to localize the pattern generations and pruning properties to remove many frequent but non-closed patterns, it can efficiently mine frequent closed patterns in 9DLT image databases. The experimental results show that our proposed algorithm is efficient and scalable, and it outperforms the 9DLT-Miner algorithm.
Subjects
data mining
image mining
closed pattern
spatial relation
9DLT string
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