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 language | English (US) |
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Title of host publication | Frequent Pattern Mining |
Publisher | Springer International Publishing |
Pages | 283-306 |
Number of pages | 24 |
Volume | 9783319078212 |
ISBN (Electronic) | 9783319078212 |
ISBN (Print) | 3319078208, 9783319078205 |
DOIs | |
State | Published - Jul 1 2014 |
All Science Journal Classification (ASJC) codes
- Computer Science(all)