Mining Closed Numerical Patterns in Spatial Databases
Date Issued
2010
Date
2010
Author(s)
Lee, Shin-Ling
Abstract
With advance in positioning technology, a large amount of spatial data has been collected into databases. How to mine frequent spatial pattern has attracted more and more attention recently. Mining numerical patterns in spatial databases can help us identify the quantification relationships between different locations to understand or predict the trends of markets. Therefore, in this thesis, we propose a novel algorithm, CNP-Mine (Closed Numerical Pattern Mining), to mine the closed numerical patterns in a spatial database. The proposed algorithm consists of two phases. First, we find all frequent patterns of length one (1-patterns) in the database and generate their projected databases for each frequent 1-pattern found. Next, we use a frequent spatial pattern tree to recursively generate frequent patterns in a DFS manner until no more frequent closed patterns can be found. During the mining process, we employ several effective pruning strategies to prune unnecessary candidates and a closure checking scheme to remove non-closed patterns. Moreover, we localize the support counting and pattern joins in projected databases. Thus, the proposed method can efficiently mine closed numerical patterns in a spatial database. The experimental results show that the CNP-Mine algorithm outperforms the modified A-Close algorithm in several orders of magnitude.
Subjects
numerical patterns
frequent patterns
closed patterns
spatial databases
data mining
Type
thesis
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