The Clusterization of Geo-Tagged Data for Finding City Sights with Use of a Modification of k-MXT Algorithm
Anastasia Stepanova, Sergei Mironov, Eugene Korobov, Sergei Sidorov
Available Online February 2019.
- https://doi.org/10.2991/cmdm-18.2019.4How to use a DOI?
- graph clusterization, weighted graph, geo-tagged data
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - Third Workshop on Computer Modelling in Decision Making (CMDM 2018) PB - Atlantis Press SN - 2352-538X UR - https://doi.org/10.2991/cmdm-18.2019.4 DO - https://doi.org/10.2991/cmdm-18.2019.4 ID - Stepanova2019/02 ER -