Periodical Skeletonization for Partially Periodic Pattern Mining

In this chapter, we describe a new periodicity detection algorithm to efficiently discover short period patterns that may exist in only a limited range of the time series. We refer to these patterns as the dense periodic patterns, where the periodicity is focused on part of the time series. We present a dense periodic pattern mining algorithm called DPMiner to find dense periodic patterns, and design a pruning strategy to limit the search space to the feasible periods. Experimental results on both real-life and synthetic datasets indicate that DPMiner is both scalable and efficient.

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Periodic Pattern Mining of Embedded Multimedia Application Traces

Lecture Notes in Electrical Engineering - Embedded and Multimedia Computing Technology and Service ◽

10.1007/978-94-007-5076-0_4 ◽

2012 ◽

pp. 29-37

Author(s):

Patricia López-Cueva ◽

Aurélie Bertaux ◽

Alexandre Termier ◽

Jean-François Méhaut ◽

Miguel Santana

Keyword(s):

Pattern Mining ◽

Multimedia Application ◽

Periodic Pattern

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APDS: A framework for discovering movement pattern from trajectory database

International Journal of Distributed Sensor Networks ◽

10.1177/1550147719888164 ◽

2019 ◽

Vol 15 (11) ◽

pp. 155014771988816

Author(s):

Guan Yuan ◽

Zhongqiu Wang ◽

Zhixiao Wang ◽

Fukai Zhang ◽

Li Yuan ◽

...

Keyword(s):

Clustering Algorithm ◽

Moving Objects ◽

Pattern Mining ◽

Pattern Discovery ◽

Movement Pattern ◽

Moving Object ◽

Periodic Pattern ◽

Trajectory Data ◽

Discovery System ◽

Spatiotemporal Data Mining

Currently, the boosting of location acquisition devices makes it possible to track all kinds of moving objects, and collect and store their trajectories in database. Therefore, how to find knowledge from huge amount of trajectory data has become an attractive topic. Movement pattern is an efficient way to understand moving objects’ behavior and analyze their habits. To promote the application of spatiotemporal data mining, a moving object activity pattern discovery system is designed and implemented in this article. First of all, raw trajectory data are preprocessed using methods like data clean, data interpolation, and compression. Second, a simplified density-based trajectory clustering algorithm is implemented to find and group similar movement patterns. Third, in order to discover the trends and periodicity of movement pattern, a trajectory periodic pattern mining algorithm is developed. Finally, comprehensive experiments with different parameters are conducted to validate the pattern discovery system. The experimental results show that the system is robust and efficient to analyze moving object trajectory data and discover useful patterns.

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