Mining Closed Line Patterns in Time-series Databases
Date Issued
2010
Date
2010
Author(s)
Tseng, Tzu-Hao
Abstract
Time series data has grown at a rapid speed and has been analyzed in various domains, e.g., financial data analysis, network traffic analysis, moving object tracking, etc. Since a line segment may contain many points, the length of a pattern represented by points is much larger than that by line segments. Thus, mining line-based patterns will be more efficient than mining point-based patterns. Therefore, in this thesis, we propose an efficient algorithm, called CLP-Mine (Closed Line Patterns Mining), to find closed line patterns in time-series databases. The proposed algorithm consists of three phases. First, we transform each sequence in the original time-series database into a sequence of line segments. Second, we find all frequent patterns of length one in the transformed database. Third, for each pattern found in the second phase, we recursively generate frequent patterns by a frequent pattern tree in a depth-first search manner. During the mining process, we employ several effective pruning strategies to eliminate impossible candidates and a closure checking scheme to remove non-closed patterns. Moreover, the CLP-Mine utilizes projected databases to localize the support counting and pattern generation. The experimental results show that the proposed method is more efficient and scalable than the modified A-Close algorithm in both synthetic and real datasets.
Subjects
line pattern
point-based pattern
closed pattern
frequent pattern
time series database
Type
thesis
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