A novel visual analytics approach for clustering large-scale social data

Zhangye Wang, Chang Chen, Juanxia Zhou, Jiyuan Liao, Wei Chen, Ross Maciejewski

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

8 Citations (Scopus)

Abstract

Social data refers to data individuals create that is knowingly and voluntarily shared by them and is an exciting avenue into gaining insight into interpersonal behaviors and interaction. However, such data is large, heterogeneous and often incomplete, properties that make the analysis of such data extremely challenging. One common method of exploring such data is through cluster analysis, which can enable analysts to find groups of related users, behaviors and interactions. This paper presents a novel visual analysis approach for detecting clusters within large-scale social networks by utilizing a divide-analyze-recombine scheme that sequentially performs data partitioning, subset clustering and result recombination within an integrated visual interface. A case study on a microblog messaging data (with 4.8 millions users) is used to demonstrate the feasibility of this approach and comparisons are also provided to illustrate the performance benefits of this approach with respect to existing solutions.

Original languageEnglish (US)
Title of host publicationProceedings - 2013 IEEE International Conference on Big Data, Big Data 2013
Pages79-86
Number of pages8
DOIs
StatePublished - 2013
Event2013 IEEE International Conference on Big Data, Big Data 2013 - Santa Clara, CA, United States
Duration: Oct 6 2013Oct 9 2013

Other

Other2013 IEEE International Conference on Big Data, Big Data 2013
CountryUnited States
CitySanta Clara, CA
Period10/6/1310/9/13

Fingerprint

Cluster analysis

Keywords

  • Cluster Analysis
  • Divide and Recombine
  • K-means
  • Visual Analysis

ASJC Scopus subject areas

  • Software

Cite this

Wang, Z., Chen, C., Zhou, J., Liao, J., Chen, W., & Maciejewski, R. (2013). A novel visual analytics approach for clustering large-scale social data. In Proceedings - 2013 IEEE International Conference on Big Data, Big Data 2013 (pp. 79-86). [6691718] https://doi.org/10.1109/BigData.2013.6691718

A novel visual analytics approach for clustering large-scale social data. / Wang, Zhangye; Chen, Chang; Zhou, Juanxia; Liao, Jiyuan; Chen, Wei; Maciejewski, Ross.

Proceedings - 2013 IEEE International Conference on Big Data, Big Data 2013. 2013. p. 79-86 6691718.

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

Wang, Z, Chen, C, Zhou, J, Liao, J, Chen, W & Maciejewski, R 2013, A novel visual analytics approach for clustering large-scale social data. in Proceedings - 2013 IEEE International Conference on Big Data, Big Data 2013., 6691718, pp. 79-86, 2013 IEEE International Conference on Big Data, Big Data 2013, Santa Clara, CA, United States, 10/6/13. https://doi.org/10.1109/BigData.2013.6691718
Wang Z, Chen C, Zhou J, Liao J, Chen W, Maciejewski R. A novel visual analytics approach for clustering large-scale social data. In Proceedings - 2013 IEEE International Conference on Big Data, Big Data 2013. 2013. p. 79-86. 6691718 https://doi.org/10.1109/BigData.2013.6691718
Wang, Zhangye ; Chen, Chang ; Zhou, Juanxia ; Liao, Jiyuan ; Chen, Wei ; Maciejewski, Ross. / A novel visual analytics approach for clustering large-scale social data. Proceedings - 2013 IEEE International Conference on Big Data, Big Data 2013. 2013. pp. 79-86
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