TY - GEN
T1 - Unsupervised sparse matrix co-clustering for marketing and sales intelligence
AU - Zouzias, Anastasios
AU - Vlachos, Michail
AU - Freris, Nikolaos M.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84861441994&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84861441994&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-30217-6_49
DO - 10.1007/978-3-642-30217-6_49
M3 - Conference contribution
AN - SCOPUS:84861441994
SN - 9783642302169
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 591
EP - 603
BT - Advances in Knowledge Discovery and Data Mining - 16th Pacific-Asia Conference, PAKDD 2012, Proceedings
T2 - 16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2012
Y2 - 29 May 2012 through 1 June 2012
ER -