Visualisation of trend pattern migrations in social networks

Nohuddin Puteri Nor Ellyza, Frans Coenen, Rob Christley, Wataru Sunayama

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In data mining process, visualisations assist the process of exploring data before modeling and exemplify the discovered knowledge into a meaningful representation. Visualisation tools are particularly useful for detecting patterns found in only small areas of the overall data. In this paper, we described a technique for discovering and presenting frequent pattern migrations in temporal social network data. The migrations are identified using the concept of aMigration Matrix and presented using a visualisation tool. The technique has been built into the Pattern Migration Identification and Visualisation (PMIV) framework which is designed to operate using trend clusters which have been extracted from big network data using a Self Organising Map technique. The PMIV is also aimed to detect changes in the characteristics of trend clusters and the existence of communities of trend clusters.

Original languageEnglish
Title of host publicationAdvances in Visual Informatics - 4th International Visual Informatics Conference, IVIC 2015, Proceedings
PublisherSpringer Verlag
Pages77-88
Number of pages12
Volume9429
ISBN (Print)9783319259383, 9783319259383
DOIs
Publication statusPublished - 2015
Event4th International Visual Informatics Conference, IVIC 2015 - Bangi, Malaysia
Duration: 17 Nov 201519 Nov 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9429
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other4th International Visual Informatics Conference, IVIC 2015
CountryMalaysia
CityBangi
Period17/11/1519/11/15

Fingerprint

Social Networks
Migration
Visualization
Frequent Pattern
Self organizing maps
Self-organizing Map
Data mining
Data structures
Data Mining
Trends
Modeling

Keywords

  • Frequent patterns
  • Self organising maps
  • Trend analysis
  • Trend clustering
  • Visualisation

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Puteri Nor Ellyza, N., Coenen, F., Christley, R., & Sunayama, W. (2015). Visualisation of trend pattern migrations in social networks. In Advances in Visual Informatics - 4th International Visual Informatics Conference, IVIC 2015, Proceedings (Vol. 9429, pp. 77-88). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9429). Springer Verlag. https://doi.org/10.1007/978-3-319-25939-0_7

Visualisation of trend pattern migrations in social networks. / Puteri Nor Ellyza, Nohuddin; Coenen, Frans; Christley, Rob; Sunayama, Wataru.

Advances in Visual Informatics - 4th International Visual Informatics Conference, IVIC 2015, Proceedings. Vol. 9429 Springer Verlag, 2015. p. 77-88 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9429).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Puteri Nor Ellyza, N, Coenen, F, Christley, R & Sunayama, W 2015, Visualisation of trend pattern migrations in social networks. in Advances in Visual Informatics - 4th International Visual Informatics Conference, IVIC 2015, Proceedings. vol. 9429, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9429, Springer Verlag, pp. 77-88, 4th International Visual Informatics Conference, IVIC 2015, Bangi, Malaysia, 17/11/15. https://doi.org/10.1007/978-3-319-25939-0_7
Puteri Nor Ellyza N, Coenen F, Christley R, Sunayama W. Visualisation of trend pattern migrations in social networks. In Advances in Visual Informatics - 4th International Visual Informatics Conference, IVIC 2015, Proceedings. Vol. 9429. Springer Verlag. 2015. p. 77-88. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-25939-0_7
Puteri Nor Ellyza, Nohuddin ; Coenen, Frans ; Christley, Rob ; Sunayama, Wataru. / Visualisation of trend pattern migrations in social networks. Advances in Visual Informatics - 4th International Visual Informatics Conference, IVIC 2015, Proceedings. Vol. 9429 Springer Verlag, 2015. pp. 77-88 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{a33295f978c046eea572846f3f649186,
title = "Visualisation of trend pattern migrations in social networks",
abstract = "In data mining process, visualisations assist the process of exploring data before modeling and exemplify the discovered knowledge into a meaningful representation. Visualisation tools are particularly useful for detecting patterns found in only small areas of the overall data. In this paper, we described a technique for discovering and presenting frequent pattern migrations in temporal social network data. The migrations are identified using the concept of aMigration Matrix and presented using a visualisation tool. The technique has been built into the Pattern Migration Identification and Visualisation (PMIV) framework which is designed to operate using trend clusters which have been extracted from big network data using a Self Organising Map technique. The PMIV is also aimed to detect changes in the characteristics of trend clusters and the existence of communities of trend clusters.",
keywords = "Frequent patterns, Self organising maps, Trend analysis, Trend clustering, Visualisation",
author = "{Puteri Nor Ellyza}, Nohuddin and Frans Coenen and Rob Christley and Wataru Sunayama",
year = "2015",
doi = "10.1007/978-3-319-25939-0_7",
language = "English",
isbn = "9783319259383",
volume = "9429",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "77--88",
booktitle = "Advances in Visual Informatics - 4th International Visual Informatics Conference, IVIC 2015, Proceedings",
address = "Germany",

}

TY - GEN

T1 - Visualisation of trend pattern migrations in social networks

AU - Puteri Nor Ellyza, Nohuddin

AU - Coenen, Frans

AU - Christley, Rob

AU - Sunayama, Wataru

PY - 2015

Y1 - 2015

N2 - In data mining process, visualisations assist the process of exploring data before modeling and exemplify the discovered knowledge into a meaningful representation. Visualisation tools are particularly useful for detecting patterns found in only small areas of the overall data. In this paper, we described a technique for discovering and presenting frequent pattern migrations in temporal social network data. The migrations are identified using the concept of aMigration Matrix and presented using a visualisation tool. The technique has been built into the Pattern Migration Identification and Visualisation (PMIV) framework which is designed to operate using trend clusters which have been extracted from big network data using a Self Organising Map technique. The PMIV is also aimed to detect changes in the characteristics of trend clusters and the existence of communities of trend clusters.

AB - In data mining process, visualisations assist the process of exploring data before modeling and exemplify the discovered knowledge into a meaningful representation. Visualisation tools are particularly useful for detecting patterns found in only small areas of the overall data. In this paper, we described a technique for discovering and presenting frequent pattern migrations in temporal social network data. The migrations are identified using the concept of aMigration Matrix and presented using a visualisation tool. The technique has been built into the Pattern Migration Identification and Visualisation (PMIV) framework which is designed to operate using trend clusters which have been extracted from big network data using a Self Organising Map technique. The PMIV is also aimed to detect changes in the characteristics of trend clusters and the existence of communities of trend clusters.

KW - Frequent patterns

KW - Self organising maps

KW - Trend analysis

KW - Trend clustering

KW - Visualisation

UR - http://www.scopus.com/inward/record.url?scp=84950106745&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84950106745&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-25939-0_7

DO - 10.1007/978-3-319-25939-0_7

M3 - Conference contribution

AN - SCOPUS:84950106745

SN - 9783319259383

SN - 9783319259383

VL - 9429

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 77

EP - 88

BT - Advances in Visual Informatics - 4th International Visual Informatics Conference, IVIC 2015, Proceedings

PB - Springer Verlag

ER -