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Algorithms 2017, 10(1), 35; doi:10.3390/a10010035

A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics

1
Institute of Informatics and Telecommunications, National Center for Scientific Research—“Demokritos”, 153 41 Athens, Greece
2
Department of Computer Engineering, Technological Educational Institute of Central Greece, 351 00 Lamia, Greece
3
Department of Informatics, Ionian University, 49 100 Corfu, Greece
This paper is an extended version of our paper published in Spyrou, E.; Psallas, A.; Charalampidis, V.; Mylonas, P. Discovering Areas of Interest using a Semantic Geo-Clustering Approach. In Proceedings of the Mining Humanistic Data Workshop (MHDW), located at the International Conference on Artificial Intelligence Applications and Innovations (AIAI), Thessaloniki, Greece, 16–18 September 2016.
*
Author to whom correspondence should be addressed.
Academic Editor: Toly Chen
Received: 20 December 2016 / Revised: 1 March 2017 / Accepted: 13 March 2017 / Published: 18 March 2017
(This article belongs to the Special Issue Humanistic Data Processing)
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Abstract

Living in the “era of social networking”, we are experiencing a data revolution, generating an astonishing amount of digital information every single day. Due to this proliferation of data volume, there has been an explosion of new application domains for information mined from social networks. In this paper, we leverage this “socially-generated knowledge” (i.e., user-generated content derived from social networks) towards the detection of areas-of-interest within an urban region. These large and homogeneous areas contain multiple points-of-interest which are of special interest to particular groups of people (e.g., tourists and/or consumers). In order to identify them, we exploit two types of metadata, namely location-based information included within geo-tagged photos that we collect from Flickr, along with plain simple textual information from user-generated tags. We propose an algorithm that divides a predefined geographical area (i.e., the center of Athens, Greece) into “tile”-shaped sub-regions and based on an iterative merging procedure, it aims to detect larger, cohesive areas. We examine the performance of the algorithm both in a qualitative and quantitative manner. Our experiments demonstrate that the proposed geo-clustering algorithm is able to correctly detect regions that contain popular tourist attractions within them with very promising results. View Full-Text
Keywords: areas of interest; semantics; geo-clustering; Flickr areas of interest; semantics; geo-clustering; Flickr
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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. (CC BY 4.0).

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Spyrou, E.; Korakakis, M.; Charalampidis, V.; Psallas, A.; Mylonas, P. A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics. Algorithms 2017, 10, 35.

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