KDD 2009 Workshop on Data Mining using Matrices and Tensors  (DMMT'09)

Held in conjunction with
The 15th ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining
(KDD 2009)

June 28th -July 1st, 2009, PARIS, FRANCE

Scope and Program
Latest News
Workshop Program (PDF) or (DOC)
Workshop Proceedings
Call for Papers (PDF) or (DOC)

Workshop Description

Topics of Interest

Important Dates

Paper Submissions




Program Committee

Relevant Links
DMKD Special Issue on Data Mining using Matrices and Graphs
DMMT'08 (with SIGKDD 2008)
Stanford Workshop on Algorithms for Modern Massive Data Sets


Latest News    


[April 11th, 2009] Confirmed Invited Speakers:  Prof. Lenore Mullin (NSF Program Director and SUNY Albany) and Prof. James Raynolds (SUNY Albany). Talk title: Tensors and n-d Arrays: Mathematics of Arrays, Psi-Calculus, and Composition of Tensor and Array Operations.

[April 13th, 2009] Confirmed Invited Speaker: Prof. Charles Elkan (University of California, San Diego). Talk title: Factorizing Matrices with Missing Entries: Alternative Approaches.

[April 15th, 2009] Confirmed Invited Speaker:  Prof. Leiven De Lathauwer (Katholieke Universiteit Leuven, Belgium); Dr. **Lathauwer** is the developer of HOSVD.  Talk title: Tensor Decompositions and Applications: a Survey. 

[April 20, 2009] Due to many requests, the deadline for paper submission has been extended to May 5th, 2009.

[May15th, 2009] The notification data has been extended to May 25th, 2009.

[June 1st, 2009] Workshop Proceedings is posted online.

[June 16th, 2009] Workshop Program is posted online.

Workshop Description    


This workshop is a continuation of the theme of SIGKDD 2008 Workshop on Data Mining using Matrices and Tensors (DMMT’08).  DMMT’08 is the first workshop on data mining using matrices and tensors held annually with the SIGKDD Conference.  Our 2008 workshop was indeed a success and more than 100 people attend the workshop.  

The field of pattern recognition, data mining and machine learning increasingly adapt methods and algorithms from advanced matrix computations, graph theory and optimization. Prominent examples are spectral clustering, non-negative matrix factorization, Principal component analysis (PCA) and Singular Value Decomposition (SVD) related clustering and dimension reduction, tensor analysis, L-1 regularization, etc. Compared to probabilistic and information theoretic approaches, matrix-based methods are fast, easy to understand and implement; they are especially suitable for parallel and distributed-memory computers to solve large scale challenging problems such as searching and extracting patterns from the entire Web. Hence the area of data mining using matrices and tensors is a popular and growing area of research activities.

This workshop will present recent advances in algorithms and methods using matrix and scientific computing/applied mathematics for modeling and analyzing massive, high-dimensional, and nonlinear-structured data. One main goal of the workshop is to bring together leading researchers on many topic areas (e.g., computer scientists, computational and applied mathematicians) to assess the state-of-the-art, share ideas, and form collaborations. We also wish to attract practitioners who seek novel ideas for applications. In summary, this workshop will strive to emphasize the following aspects:

  • Presenting recent advances in algorithms and methods using matrix and scientific computing/applied mathematics
  • Addressing the fundamental challenges in data mining using matrices and tensors
  • Identifying killer applications and key industry drivers (where theories and applications meet)
  • Fostering interactions among researchers (from different backgrounds) sharing the same interest to promote cross-fertilization of ideas.
  • Exploring benchmark data for better evaluation of the techniques

Topics Areas    Top


Topic areas for the workshop include (but are not limited to) the following:

Methods and algorithms:

  • Principal Component Analysis and Singular value decomposition for clustering and dimension reduction
  • Nonnegative matrix factorization for unsupervised and semi-supervised learning
  • Spectral graph clustering
  • L-1 Regularization and Sparsification
  • Sparse PCA and SVD
  • Randomized algorithms for matrix computation
  • Web search and ranking algorithms
  • Canonical Decompositions (CANDECOMP/PARAFAC)
  • Tensor analysis: Rank-1 Decomposition, PARAFAC/CANDECOMP, GLRAM/2DSVD,
    Tucker decompositions (e.g., the Higher-Order SVD)
  • GSVD for classification
  • Latent Semantic Indexing and other developments for Information Retrieval
  • Linear, quadratic and semi-definite Programming
  • Non-linear manifold learning and dimension reduction
  • Computational statistics involving matrix computations
  • Feature selection and extraction
  • Graph-based learning (classification, semi-supervised learning and unsupervised learning)

Application areas

  • Information search and extraction from Web
  • Text processing and information retrieval
  • Image processing and analysis
  • Genomics and Bioinformatics
  • Scientific computing and computational sciences
  • Social Networks

Important Dates    Top

  •  April 20, 2009 May 5th, 2009:   Electronic submission of full papers
  •  May 15th, 2009 May 25th, 2009:  Author notification
  •  May 20th, 2009 May  29th, 2009:  Submission of Camera-ready papers
  •  June 28th, 2009: Workshop in Paris, France

Paper Submissions    Top

The electronic submission web site for research papers is available at: http://www.easychair.org/conferences/?conf=dmmt09.

Please register at Easychair first if you did not use EasyChair before.

Papers should be at most 10 pages long, single-spaced, in KDD conference format, in font size 10 or larger with 1-inch margins on all sides.

Workshop Organizers    Top

Workshop Co-Chairs:






Chris Ding

University of Texas at Arlington

Department of Computer Science and Engineering, 416 Yates Street, Arlington, TX 76019, USA



Tao Li

Florida International University

School of Computer Science, ECS 318 Miami, FL 33199, U.S.A.


(305) 348-6036

Publicity Chair:

                       Fei Wang,   School of Computer Science,  Florida International University

Note: for inquiries please send e-mail to taoli AT cs.fiu.edu.

Program Committee Members (tentative)   Top

  • Tammy Kolda, Sandia National Labs

  • Jesse Barlow, Penn State University

  • Michael Berry, University of Tennessee

  • Yun Chi, NEC Laboratories America

  • Lars Elden, Linkping University, Sweden

  • Christos Faloutsos, Carnegie Mellon University

  • Estratis Gallopoulos, University of Patras

  • Joydeep Ghosh, University of Texas at Austin

  • Ming Gu, University of Califonia, Berkeley

  • Michael Jordan, University of California, Berkeley

  • Huan Liu, Arizona State University

  • Michael Ng, Hong Kong Baptist University

  • Haesun Park, Georgia Tech

  • Wei Peng, Xerox Research

  • Robert Plemmons, Wake Forest

  • Alex Pothen, Old Domino University

  • Yousef Saad, University of Minnesota

  • Horst Simon, Lawrence Berkeley National Laboratory

  • Gang Wang, Microsoft Research

  • Fei Wang, Florida International University

  • Kai Yu, NEC Laboratories America

  • Hongyuan Zha, Georgia Tech

  • Zhongyuan Zhang, Central University of Finance & Economics

  • Shenghuo Zhu, NEC Laboratories America