Using Modified Periodicity Transform for Partially Periodic Pattern Mining

ASIAIR > College of Computer Science > Department of Computer Science and Information Engineering > Theses & dissertations > Item 310904400/3924

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Title:	Using Modified Periodicity Transform for Partially Periodic Pattern Mining
Authors:	Tsai Meng-Shu
Contributors:	Department of Computer Science and Information Engineering
Keywords:	time series analysis;partially periodic pattern mining;periodic pattern mining;data mining
Date:	2004
Issue Date:	2009-11-18 13:13:11 (UTC+0)
Publisher:	Asia University
Abstract:	Numerous efforts had been made on periodic pattern mining in the past years. The problem of partially periodic pattern mining is similar to that of periodic pattern mining; however, the occurrences of patterns in sequences are different. Unlike periodic patterns, partially periodic patterns usually yield a result of period missing and/or shifting. Many approaches to partially periodic pattern mining had been proposed in the literature, but did not reach a satisfactory result in terms of computation time and memory accessibility. Besides, most approaches require given patterns for the mining tasks. On the basis of periodicity transforms, the modified periodicity transforms (MPT) and modified periodicity transforms without candidate pattern (MPT_NNC) are proposed in the study for discovering partially periodic patterns in sequences. Moreover, an efficient method for automatic generation of candidate patterns without giving domain knowledge is proposed as well. It is based on the location and possibility of the basis elements in a sequence and can be regarded as an auxiliary approach to the MPT and MPT_NNC algorithms.
Appears in Collections:	[Department of Computer Science and Information Engineering] Theses & dissertations

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