Extracting Attractive Local-Area Topics in Georeferenced Documents using a New Density-based Spatial Clustering Algorithm

Along with the popularization of social media, huge numbers of georeferenced documents (which include location information) are being posted on social media sites via the Internet, allowing people to transmit and collect geographic information. Typically, such georeferenced documents are related not...

Full description

Saved in:
Bibliographic Details
Published in:IAENG international journal of computer science Vol. 41; no. 3; pp. 185 - 192
Main Authors: Sakai, Tatsuhiro, Tamura, Keiichi, Kitakami, Hajime
Format: Journal Article
Language:English
Published: 01.09.2014
Subjects:
Algorithms
Clustering
Clusters
Digital media
Internet
Recognition
Social networks
ISSN:1819-656X, 1819-9224
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract Along with the popularization of social media, huge numbers of georeferenced documents (which include location information) are being posted on social media sites via the Internet, allowing people to transmit and collect geographic information. Typically, such georeferenced documents are related not only to personal topics but also to local topics and events. Therefore, extracting "attractive" areas associated with local topics from georeferenced documents is currently one of the most important challenges in different application domains. In this paper, a novel spatial clustering algorithm for extracting "attractive" local-area topics in georeferenced documents, known as the ([epsilon], [sigma])-density-based spatial clustering algorithm, is proposed. We defined a new type of spatial cluster called an ([epsilon], [sigma])-density-based spatial cluster. The proposed density-based spatial clustering algorithm can recognize both semantically and spatially separated spatial clusters. Therefore, the proposed algorithm can extract "attractive" local-area topics as ([epsilon], [sigma])-density-based spatial clusters. To evaluate our proposed clustering algorithm, geo-tagged tweets posted on the Twitter site were used. The experimental results showed that the ([epsilon], [sigma])-density-based spatial clustering algorithm could extract "attractive" areas as the ([epsilon], [sigma])-density-based spatial clusters that were closely related to local topics.
AbstractList Along with the popularization of social media, huge numbers of georeferenced documents (which include location information) are being posted on social media sites via the Internet, allowing people to transmit and collect geographic information. Typically, such georeferenced documents are related not only to personal topics but also to local topics and events. Therefore, extracting "attractive" areas associated with local topics from georeferenced documents is currently one of the most important challenges in different application domains. In this paper, a novel spatial clustering algorithm for extracting "attractive" local-area topics in georeferenced documents, known as the ([epsilon], [sigma])-density-based spatial clustering algorithm, is proposed. We defined a new type of spatial cluster called an ([epsilon], [sigma])-density-based spatial cluster. The proposed density-based spatial clustering algorithm can recognize both semantically and spatially separated spatial clusters. Therefore, the proposed algorithm can extract "attractive" local-area topics as ([epsilon], [sigma])-density-based spatial clusters. To evaluate our proposed clustering algorithm, geo-tagged tweets posted on the Twitter site were used. The experimental results showed that the ([epsilon], [sigma])-density-based spatial clustering algorithm could extract "attractive" areas as the ([epsilon], [sigma])-density-based spatial clusters that were closely related to local topics.
Author Sakai, Tatsuhiro
Tamura, Keiichi
Kitakami, Hajime
Author_xml – sequence: 1
  givenname: Tatsuhiro
  surname: Sakai
  fullname: Sakai, Tatsuhiro
– sequence: 2
  givenname: Keiichi
  surname: Tamura
  fullname: Tamura, Keiichi
– sequence: 3
  givenname: Hajime
  surname: Kitakami
  fullname: Kitakami, Hajime
BookMark eNotjU1PAjEURRuDiYj8hy7dTEI_p7MkgGhCdCEm7sibzhtsMrRj21H596Kwumdx7r23ZOSDxysyZoZVRcW5HF1YK_1-Q6YpuXomZSmMUWJMjqufHMFm5_d0ns_4hXQTLHTFPCLQbeidTdR5usYQscWI3mJDl8EOB_Q50SH9tYE-4zddok8uH4sa0sl57SE76OiiG1LG-H_S7UN0-eNwR65b6BJOLzkhbw-r7eKx2LysnxbzTdGzmcgFlrwSjJXG1o3UCgRI1LzUFngjDBhraqkUQ6kbpQ2wVpUtVtieVAOtBDEh9-fdPobPAVPeHVyy2HXgMQxpx7TknGutmPgFAL5gTQ
ContentType Journal Article
DBID 7SC
8FD
JQ2
L7M
L~C
L~D
DatabaseName Computer and Information Systems Abstracts
Technology Research Database
ProQuest Computer Science Collection
Advanced Technologies Database with Aerospace
Computer and Information Systems Abstracts – Academic
Computer and Information Systems Abstracts Professional
DatabaseTitle Computer and Information Systems Abstracts
Technology Research Database
Computer and Information Systems Abstracts – Academic
Advanced Technologies Database with Aerospace
ProQuest Computer Science Collection
Computer and Information Systems Abstracts Professional
DatabaseTitleList Computer and Information Systems Abstracts
DeliveryMethod fulltext_linktorsrc
Discipline Computer Science
EISSN 1819-9224
EndPage 192
GroupedDBID .4S
.DC
2WC
5VS
7SC
8FD
AAKPC
ADMLS
ALMA_UNASSIGNED_HOLDINGS
ARCSS
EDO
EOJEC
I-F
JQ2
KQ8
L7M
L~C
L~D
MK~
ML~
OBODZ
OK1
OVT
P2P
TR2
TUS
ID FETCH-LOGICAL-p103t-e72931178cbd465a3a4e6276ca2d38a8c8b4551e46d568a1f57fe9ef65a8af4a3
ISSN 1819-656X
IngestDate Fri Jul 11 07:36:59 EDT 2025
IsPeerReviewed true
IsScholarly true
Issue 3
Language English
LinkModel OpenURL
MergedId FETCHMERGED-LOGICAL-p103t-e72931178cbd465a3a4e6276ca2d38a8c8b4551e46d568a1f57fe9ef65a8af4a3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
PQID 1642226651
PQPubID 23500
PageCount 8
ParticipantIDs proquest_miscellaneous_1642226651
PublicationCentury 2000
PublicationDate 20140901
PublicationDateYYYYMMDD 2014-09-01
PublicationDate_xml – month: 09
  year: 2014
  text: 20140901
  day: 01
PublicationDecade 2010
PublicationTitle IAENG international journal of computer science
PublicationYear 2014
SSID ssib044738853
ssj0070001
Score 1.9685459
Snippet Along with the popularization of social media, huge numbers of georeferenced documents (which include location information) are being posted on social media...
SourceID proquest
SourceType Aggregation Database
StartPage 185
SubjectTerms Algorithms
Clustering
Clusters
Digital media
Internet
Recognition
Social networks
Title Extracting Attractive Local-Area Topics in Georeferenced Documents using a New Density-based Spatial Clustering Algorithm
URI https://www.proquest.com/docview/1642226651
Volume 41
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVHPJ
  databaseName: ROAD: Directory of Open Access Scholarly Resources
  customDbUrl:
  eissn: 1819-9224
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssib044738853
  issn: 1819-656X
  databaseCode: M~E
  dateStart: 0
  isFulltext: true
  titleUrlDefault: https://road.issn.org
  providerName: ISSN International Centre
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Nj9MwELW6Kw5c-EbAAjIS4lJZimvHSY4VdAGpFA5ZqbfKcRwaaNOSJlX3slf-NmM7H624LAcuUWolbZV5mnmezMxD6C1nntQ0igiVghGuZUQSLSRRPPUSlmqWKjtdfxrMZuF8Hn0bDH63vTD7VVAU4eEQbf-rqWENjG1aZ__B3N2XwgKcg9HhCGaH460MPzlUtvPJpDsqd7rXw6mJWWQMFHEYb7ZmNHNe2IR4O2g2BS6tatfwVtsEgrTFjx9MhXt1TUy4A25qCrBNvmFVmwkL9kdW3zdlXi3Xxzz383gy-2hnUfTpxqMhFarRkhg2AbjL88ifTh47ltWuXublps8srOvSta_pPFfLvC8eqOCmtRPflj_yZvhCk8igvKvUan0vkBMC9HLuQlO_Fo1cm3XrsDk9AiY78r7Uqf80gZw6kb3TGduzr4vLq-l0EU_m8bvtL2Lkx8xr-kaL5QydMc9IMHy5mbRuifOAhZbVuAAfGF5s9vHt__0rjFtuEj9A95pNBR47MDxEA108QvdbwQ7c-O_H6LrHBu6xgXtsYIcNnBf4BBu4wwa22MASAzbwCTZwgw3cYwN32HiCri4n8ftPpNHeIFvqsYpo2HQxSoNQJSkXvmSSazEKhJKjlIUyVGHCgWxrLlJfhJJmfpDpSGdwaSgzLtlTdF5sCv0M4ZE_Sn2aZJyGgidMRFniqYh5SkfwWSfP0Zv28S3At5kXVrLQm3q3gK080FchfPriFtdcoLs9rF6i86qs9St0R-2rfFe-tib9A2MhfOI
linkProvider ISSN International Centre
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Extracting+Attractive+Local-Area+Topics+in+Georeferenced+Documents+using+a+New+Density-based+Spatial+Clustering+Algorithm&rft.jtitle=IAENG+international+journal+of+computer+science&rft.au=Sakai%2C+Tatsuhiro&rft.au=Tamura%2C+Keiichi&rft.au=Kitakami%2C+Hajime&rft.date=2014-09-01&rft.issn=1819-656X&rft.eissn=1819-9224&rft.volume=41&rft.issue=3&rft.spage=185&rft.epage=192&rft.externalDBID=NO_FULL_TEXT
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1819-656X&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1819-656X&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1819-656X&client=summon