Spatiotemporal pattern mining: Algorithms and applications

Research output: Chapter in Book/Report/Conference proceedingChapter

16 Scopus citations

Abstract

With the fast development of positioning technology, spatiotemporal data has become widely available nowadays. Mining patterns from spatiotemporal data has many important applications in human mobility understanding, smart transportation, urban planning and ecological studies. In this chapter, we provide an overview of spatiotemporal data mining methods. We classify the patterns into three categories: (1) individual periodic pattern; (2) pairwise movement pattern and (3) aggregative patterns over multiple trajectories. This chapter states the challenges of pattern discovery, reviews the state-of-the-art methods and also discusses the limitations of existing methods.

Original languageEnglish (US)
Title of host publicationFrequent Pattern Mining
PublisherSpringer International Publishing
Pages283-306
Number of pages24
Volume9783319078212
ISBN (Electronic)9783319078212
ISBN (Print)3319078208, 9783319078205
DOIs
StatePublished - Jul 1 2014

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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