Splitter: Mining Fine-Grained Sequential Patterns in Semantic Trajectories

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Provided by: VLD Digital
Topic: Big Data
Format: PDF
Driven by the advance of positioning technology and the popularity of location-sharing services, semantic-enriched trajectory data have become unprecedentedly available. The sequential patterns hidden in such data, when properly defined and extracted, can greatly benefit tasks like targeted advertising and urban planning. Unfortunately, classic sequential pattern mining algorithms developed for transactional data cannot effectively mine patterns in semantic trajectories, mainly because the places in the continuous space cannot be regarded as independent \"Items\". Instead, similar places need to be grouped to collaboratively form frequent sequential patterns.
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