Mining Multi-resolution Frequent Patterns in Time-series Databases
Date Issued
2009
Date
2009
Author(s)
Tzeng, Huei-Ping
Abstract
Time series data have been generated at an unprecedented speed from almost every application domain in the last decade, e.g., financial data analysis, network traffic analysis, scientific data processing, etc. Mining multi-resolution frequent patterns in time series databases can help scientists or financial analysts analyze the trends of data and obtain valuable information. Therefore, in this thesis, we propose an efficient algorithm, MFP-Miner (Mining Frequent Patterns Miner), to mine multi-resolution frequent patterns in time-series databases. Our proposed method consists of three phases. First, we transform the original database into a database in the low resolution and obtain the transformed database. Second, we find frequent 1-patterns from the transformed database and construct a projected database for each frequent 1-pattern found. Third, we recursively generate frequent patterns by a frequent pattern tree in a depth-first search manner and enumerate all frequent patterns in the original database. Since the MFP-Miner employs projected databases to localize the support counting and pattern mining, and utilizes effective pruning strategies to remove unnecessary candidates during the mining process, it can efficiently mine all multi-resolution frequent patterns in time-series databases. The experiment results show that the proposed method is more efficient and scalable than the Apriori modified.
Subjects
data mining
time series database
frequent patterns
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