Density-Based Spatiotemporal Clustering Algorithm for Extracting Bursty Areas from Georeferenced Documents

Nowadays, with the increasing attention being paid to social media, a huge number of georeferenced documents, which include location information, are posted on social media sites. People transmit and collect information over the Internet through these georeferenced documents. Georeferenced documents...

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Bibliographic Details
Published in:2013 IEEE International Conference on Systems, Man, and Cybernetics pp. 2079 - 2084
Main Authors: Tamura, Keiichi, Ichimura, Takumi
Format: Conference Proceeding
Language:English
Published: IEEE 01.10.2013
Subjects:
Clustering algorithms
Density-based clustering
Georeferenced document
Internet
Media
Rain
Snow
Spatiotemporal clustering algorithm
Spatiotemporal data stream
Spatiotemporal phenomena
Topic and event detection
Twitter
ISSN:1062-922X
Online Access:Get full text
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Summary:Nowadays, with the increasing attention being paid to social media, a huge number of georeferenced documents, which include location information, are posted on social media sites. People transmit and collect information over the Internet through these georeferenced documents. Georeferenced documents are usually related to not only personal topics but also local topics and events. Therefore, extracting bursty areas associated with local topics and events from georeferenced documents is one of the most important challenges in different application domains. In this paper, a novel spatiotemporal clustering algorithm, called the (ϵ,τ)-density-based spatiotemporal clustering algorithm, for extracting bursty areas from georeferenced documents is proposed. The proposed clustering algorithm can recognize not only temporally-separated but also spatially-separated clusters. To evaluate our proposed clustering algorithm, geo-tagged tweets posted on the Twitter site are used. The experimental results show that the (ϵ,τ)-density-based spatiotemporal clustering algorithm can extract bursty areas as (ϵ,τ)-density-based spatiotemporal clusters associated with local topics and events.
ISSN:1062-922X
DOI:10.1109/SMC.2013.356