Publication Type:

Book Chapter

Source:

Artificial Intelligence and Evolutionary Algorithms in Engineering Systems: Proceedings of ICAEES 2014, Volume 1, Springer India, Number 324, New Delhi, p.119–127 (2015)

ISBN:

9788132221265

URL:

http://dx.doi.org/10.1007/978-81-322-2126-5_14

Keywords:

GMPMINE and cluster ensemble algorithm, HMM, Location-based services, Personal communication system, Trajectory mining algorithms

Abstract:

This paper is a research and analysis on the prediction of location of moving objects that gained popularity over the years. Trajectory specifies the path of the movement of any object. There is an increase in the number of applications using the location-based services (LBS), which needs to know the location of moving objects where trajectory mining plays a vital role. Trajectory mining techniques use the geographical location, semantics, and properties of the moving object to predict the location and behavior of the object. This paper analyses the various strategies in the process of making prediction of future location and constructing the trajectory pattern. The analyses of various mechanisms are done based on various factors including accuracy and ability to predict the distant future. Location prediction problem can be with known reference points and unknown reference points, and semantic-based prediction gives an accurate result whereas the probability-based prediction for unknown reference points.

Cite this Research Publication

B.A. Sabarish, R. Karthi, and Dr. Gireesh K. T., “A Survey of Location Prediction Using Trajectory Mining”, in Artificial Intelligence and Evolutionary Algorithms in Engineering Systems: Proceedings of ICAEES 2014, Volume 1, P. L. Suresh, Dash, S. Subhransu, and Panigrahi, K. Bijaya, Eds. New Delhi: Springer India, 2015, pp. 119–127.