A Scalable Biclustering Method for Heterogeneous Medical Data

  • Maxence VandrommeEmail author
  • Julie Jacques
  • Julien Taillard
  • Laetitia Jourdan
  • Clarisse Dhaenens
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10122)


We define the problem of biclustering on heterogeneous data, that is, data of various types (binary, numeric, etc.). This problem has not yet been investigated in the biclustering literature. We propose a new method, HBC (Heterogeneous BiClustering), designed to extract biclusters from heterogeneous, large-scale, sparse data matrices. The goal of this method is to handle medical data gathered by hospitals (on patients, stays, acts, diagnoses, prescriptions, etc.) and to provide valuable insight on such data. HBC takes advantage of the data sparsity and uses a constructive greedy heuristic to build a large number of possibly overlapping biclusters. The proposed method is successfully compared with a standard biclustering algorithm on small-size numeric data. Experiments on real-life data sets further assert its scalability and efficiency.


Heterogeneous Data Data Mining Task Biclustering Algorithm High Sparsity Biological Data Analysis 
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 International Publishing AG 2016

Authors and Affiliations

  • Maxence Vandromme
    • 1
    • 2
    • 3
    Email author
  • Julie Jacques
    • 1
  • Julien Taillard
    • 1
  • Laetitia Jourdan
    • 2
    • 3
  • Clarisse Dhaenens
    • 2
    • 3
  1. 1.AlicanteSeclinFrance
  2. 2.CRIStALUMR 9189, University of Lille, CNRS, Centrale LilleVilleneuve d’ascqFrance
  3. 3.INRIA Lille - Nord EuropeVilleneuve d’ascqFrance

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