Workshop Title: KDD 2009 Workshop on Data Mining using Matrices and Tensors (DMMT’09)

 

Workshop Homepage: http://www.cs.fiu.edu/~taoli/kdd09-workshop/

 

1. Introduction

 

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 are 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. 

 

The 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 attended  the workshop.  

 

2. Topic areas

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
  • 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

3.  Paper Submission

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

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.

 

4.  Important Dates

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

5.     Workshop Organizers

 

Chris Ding, University of Texas at Arlington

Tao Li, Florida International University

 

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