Unsupervised sparse matrix co-clustering for marketing and sales intelligence

Anastasios Zouzias, Michail Vlachos, Nikolaos M. Freris

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


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.

Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 16th Pacific-Asia Conference, PAKDD 2012, Proceedings
Number of pages13
EditionPART 1
StatePublished - 2012
Event16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2012 - Kuala Lumpur, Malaysia
Duration: May 29 2012Jun 1 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7301 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2012
CityKuala Lumpur

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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