Mining Closed Patterns in Multi-sequence Time-series Databases
Date Issued
2007
Date
2007
Author(s)
Lee, Tzu-Yu
DOI
en-US
Abstract
There are many algorithms proposed to find sequential patterns in a sequence database. However, the sequential pattern mining algorithms proposed are not suitable for mining frequent patterns in a time-series database since they do not consider multiple sequences in a transaction and the time intervals between the itemsets in a frequent pattern. Therefore, in this thesis, we propose an efficient algorithm, called CMP-Miner, to mine closed patterns in time-series databases where each transaction contains multiple sequences. Our proposed algorithm consists of three phases. First, we transform each time-series sequence to a symbolic sequence. Second, we generate all frequent patterns and check whether the frequent patterns are closed during the process of pattern generation. The second phase is repeated until no more closed patterns can be generated. The experimental results show that our proposed algorithm is efficient and scalable, and outperforms the modified Apriori algorithm by one order of magnitude.
Subjects
資料探勘
序列樣式
時間序列
封閉性樣式
演算法
data mining
sequential pattern
time-series sequence
closed pattern
algorithm
Type
other
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