MasterMovelets: discovering heterogeneous movelets for multiple aspect trajectory classification

Autor: Luis Otavio Alvares, Vania Bogorny, Willian Zalewski, Carlos Andres Ferrero, Lucas May Petry, Camila Leite da Silva
Rok vydání: 2020
Předmět:
Zdroj: Data Mining and Knowledge Discovery. 34:652-680
ISSN: 1573-756X
1384-5810
DOI: 10.1007/s10618-020-00676-x
Popis: In the last few years trajectory classification has been applied to many real problems, basically considering the dimensions of space and time or attributes inferred from these dimensions. However, with the explosion of social media data and the advances in the semantic enrichment of mobility data, a new type of trajectory data has emerged, and the trajectory spatio-temporal points have now multiple and heterogeneous semantic dimensions. By semantic dimensions we mean any type of information that is neither spatial nor temporal. As a consequence, new classification methods are needed to deal with this new type of data. The main challenge is how to automatically select and combine the data dimensions and to discover the subtrajectories that better discriminate the class. In this paper we propose MasterMovelets, a new parameter-free method for trajectory classification which finds the best trajectory partition and dimension combination for robust high dimensional trajectory classification. Experimental results show that our approach outperforms state-of-the-art methods by reducing the classification error up to $$63\%$$ , indicating that our proposal is very promising for multidimensional sequence data classification.
Databáze: OpenAIRE
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