The Clusterization of Geo-Tagged Data for Finding City Sights with Use of a Modification of k-MXT Algorithm

DOI

10.2991/cmdm-18.2019.4How to use a DOI?

Keywords

graph clusterization, weighted graph, geo-tagged data

Abstract

The paper considers the task of detection of the most attractive for tourists city sights using the database with geo-tagged data of photographs.We form a graph on the basis of the geo-tagged spot coordinates and rewrite the problem as the graph clusterization task. In our work we use two clustering algorithms, DBSCAN and $ k $-MXT. Moreover, we develop a modification of the $k$-MXT algorithm called the $k$-MXT-Gauss algorithm, where the calculation of the weights of the graph edges is transformed using the Gaussian distribution density. We compare the performance of $k$-MXT-Gauss algorithm with the performance of $k$-MXT and DBSCAN algorithms both on simulated data and real data.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Anastasia Stepanova
AU  - Sergei Mironov
AU  - Eugene Korobov
AU  - Sergei Sidorov
PY  - 2019/02
DA  - 2019/02
TI  - The Clusterization of Geo-Tagged Data for Finding City Sights with Use of a Modification of k-MXT Algorithm
BT  - Proceedings of the Third Workshop on Computer Modelling in Decision Making (CMDM 2018)
PB  - Atlantis Press
SP  - 20
EP  - 25
SN  - 2352-538X
UR  - https://doi.org/10.2991/cmdm-18.2019.4
DO  - 10.2991/cmdm-18.2019.4
ID  - Stepanova2019/02
ER  -