Discover spatio-temporal cluster from trajectory data enhance by heterogeneous contextual knowledge using FCA and the NᴇxᴛPʀɪᴏʀɪᴛʏCᴏɴᴄᴇᴘᴛ Algorithm
Abstract
The rising number of different kinds of data that can be used to describe a human trajectory (Such as GPS Coordinate, GSM, RFID, RSSI...) put in the spotlight the semantically rich trajectory. A semantic trajectory annotates semantic knowledge directly into raw data based on features of the studied area such as point of interest or weather conditions. One of the challenges of mobility studies nowadays is to find the right data model to shape all those data coming from different source into a framework flexible enough to multiply the contextual data that can be used; where contextual data are knowledge coming from external data source (public city dataset, web pages, national weather services etc....). Such data models are the key component of mobility studies, but oftentimes lose the computational aspect of trajectories. In this paper, we will use the semantically rich trajectory as a way to analyse behavioral data enriched by contextual knowledge as this issue has rarely been addressed in the state of the art. We will study the use of formal concept analysis and pattern mining as a way to compute complex sequential patterns in a dataset of semantic trajectories by using the NextPriorityConcept algorithm. This kind of formal concept analysis allows an interactive analysis between individuals path and contextual data resulting in a hierarchy of spatio-temporal clusters where each cluster contains a specific pattern depicting the trajectories within.