A Matrix Factorization Approach for Integrating Multiple Data Views

  • Derek Greene
  • Pádraig Cunningham
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5781)


In many domains there will exist different representations or “views” describing the same set of objects. Taken alone, these views will often be deficient or incomplete. Therefore a key problem for exploratory data analysis is the integration of multiple views to discover the underlying structures in a domain. This problem is made more difficult when disagreement exists between views. We introduce a new unsupervised algorithm for combining information from related views, using a late integration strategy. Combination is performed by applying an approach based on matrix factorization to group related clusters produced on individual views. This yields a projection of the original clusters in the form of a new set of “meta-clusters” covering the entire domain. We also provide a novel model selection strategy for identifying the correct number of meta-clusters. Evaluations performed on a number of multi-view text clustering problems demonstrate the effectiveness of the algorithm.


Base Clusterings Individual View View Versus Cumulative Vote 3sources Collection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Derek Greene
    • 1
  • Pádraig Cunningham
    • 1
  1. 1.School of Computer Science & InformaticsUniversity College DublinRepublic of Ireland

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