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Unsupervised Sparse Matrix Co-clustering for Marketing and Sales Intelligence

  • Anastasios Zouzias
  • Michail Vlachos
  • Nikolaos M. Freris
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7301)

Abstract

Business intelligence focuses on the discovery of useful retail patterns by combining both historical and prognostic data. Ultimate goal is the orchestration of more targeted sales and marketing efforts. A frequent analytic task includes the discovery of associations between customers and products. Matrix co-clustering techniques represent a common abstraction for solving this problem. We identify shortcomings of previous approaches, such as the explicit input for the number of co-clusters and the common assumption for existence of a block-diagonal matrix form. We address both of these issues and present techniques for automated matrix co-clustering. We formulate the problem as a recursive bisection on Fiedler vectors in conjunction with an eigengap-driven termination criterion. Our technique does not assume perfect block-diagonal matrix structure after reordering. We explore and identify off-diagonal cluster structures by devising a Gaussian-based density estimator. Finally, we show how to explicitly couple co-clustering with product recommendations, using real-world business intelligence data. The final outcome is a robust co-clustering algorithm that can discover in an automatic manner both disjoint and overlapping cluster structures, even in the preserve of noisy observations.

Keywords

Bipartite Graph Input Matrix Business Intelligence Product Recommendation Spectral Graph Theory 
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 2012

Authors and Affiliations

  • Anastasios Zouzias
    • 1
  • Michail Vlachos
    • 2
  • Nikolaos M. Freris
    • 2
  1. 1.Department of Computer ScienceUniversity of TorontoCanada
  2. 2.IBM Zürich Research LaboratorySwitzerland

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