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Social link prediction using category based location history in trajectory data

Revathy S B1 , emya R2

  1. College Of Engineering Perumon, APJ Abdul Kalam Technological University,kerala ,India.
  2. College Of Engineering Perumon, APJ Abdul Kalam Technological University,kerala ,India.

Correspondence should be addressed to: revathysinoj244@gmail.com.

Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-11 , Page no. 167-170, Nov-2017

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v5i11.167170

Online published on Nov 30, 2017

Copyright © Revathy S B, Remya R . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Revathy S B, Remya R, “Social link prediction using category based location history in trajectory data,” International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.167-170, 2017.

MLA Style Citation: Revathy S B, Remya R "Social link prediction using category based location history in trajectory data." International Journal of Computer Sciences and Engineering 5.11 (2017): 167-170.

APA Style Citation: Revathy S B, Remya R, (2017). Social link prediction using category based location history in trajectory data. International Journal of Computer Sciences and Engineering, 5(11), 167-170.

BibTex Style Citation:
@article{B_2017,
author = {Revathy S B, Remya R},
title = {Social link prediction using category based location history in trajectory data},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {11 2017},
volume = {5},
Issue = {11},
month = {11},
year = {2017},
issn = {2347-2693},
pages = {167-170},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1560},
doi = {https://doi.org/10.26438/ijcse/v5i11.167170}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v5i11.167170}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1560
TI - Social link prediction using category based location history in trajectory data
T2 - International Journal of Computer Sciences and Engineering
AU - Revathy S B, Remya R
PY - 2017
DA - 2017/11/30
PB - IJCSE, Indore, INDIA
SP - 167-170
IS - 11
VL - 5
SN - 2347-2693
ER -

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Abstract

With the rising popularity of location-based services, trajectory data mining became an important research topic. There exists many data mining algorithms for systematic processing, managing and mining of trajectory data. Trajectory data mining has many applications such as location recommandations, social link prediction, movement behaviour analysis etc. Here proposes a contextual trajectory analysis model which provides a flexible way to characterize the complex moving nature of humans. It embed multiple contextual information for efficiently modeling data. It includes user-level, trajectory-level, location-level, and temporal-level contexts. It can be used to predict the future location of a user based on the previous travelling pattern. Social link prediction aims to find out whether there exists reciprocal link between two users. Here also propose a method for social link prediction from trajectory data by analyzing the nearest neighbour. This method considers the tf-idf metrics as the baseline.

Key-Words / Index Term

Trajectory,contextual information,social link prediction

References

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