Hybrid clustering by integrating text and citation based graphs in journal database analysis

Xinhai Liu, Shi Yu, Yves Moreau, Frizo Janssens, Bart De Moor, Wolfgang Glänzel

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

Abstract

We propose a hybrid clustering strategy by integrating heterogeneous information sources as graphs. The hybrid clustering method is extended on the basis of modularity based Louvain method. We introduce two different approaches, graph coupling and graph fusion. The weights of these combined graphs are optimized with the criterion of maximizing the Average Normalized Mutual Information( ANMI). The methods are applied to obtain structural mapping of large scale Web of Science (WoS) journal database by integrating attribute based textual information and relation based citation information. From the experimental, the proposed graph combination scheme is compared with individual graph clustering, spectral clustering and Vector Space Model(VSM) based clustering methods.

Original languageEnglish
Title of host publicationICDM Workshops 2009 - IEEE International Conference on Data Mining
Pages521-526
Number of pages6
DOIs
Publication statusPublished - Dec 1 2009
Event2009 IEEE International Conference on Data Mining Workshops, ICDMW 2009 - Miami, FL, United States
Duration: Dec 6 2009Dec 6 2009

Publication series

NameICDM Workshops 2009 - IEEE International Conference on Data Mining

Other

Other2009 IEEE International Conference on Data Mining Workshops, ICDMW 2009
CountryUnited States
CityMiami, FL
Period12/6/0912/6/09

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ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Software

Cite this

Liu, X., Yu, S., Moreau, Y., Janssens, F., De Moor, B., & Glänzel, W. (2009). Hybrid clustering by integrating text and citation based graphs in journal database analysis. In ICDM Workshops 2009 - IEEE International Conference on Data Mining (pp. 521-526). [5360463] (ICDM Workshops 2009 - IEEE International Conference on Data Mining). https://doi.org/10.1l09/ICDMW.2009.65