Randomized Algorithms for Matrices and Massive Data Sets
Petros Drineas and Michael W. Mahoney

From Unsupervised to Semi-supervised Learning: Algorithms and evaluation approaches
Dimitrios Gunopulos, Michalis Vazirgiannis, and Maria Halkidi


The SIAM Data Mining (SDM06) Organizing Committee invites proposals for tutorials to be held in conjunction with the conference. Tutorials are an effective way to educate and/or provide the necessary background to the intended audience enabling them to understand technical advances.

For SDM06, we are seeking proposals for tutorials on all topics related to data mining. A tutorial may be a theme-oriented comprehensive survey, discuss novel data mining techniques or may center around successful and timely application of data mining in important application areas (e.g. medicine, national security, scientific data analysis). For examples of typical SIAM tutorials, see the set of accepted
tutorials at previous SIAM conferences (SDM01 - SDM05).

Tutorials are open to all conference attendees without any extra fees. The typical tutorial will be 2 hrs long (longer tutorials will be considered), and held in parallel with two paper presentation tracks during the main conference program. This format encourages participation. Previous SDM conference attracted up to 100 attendees in a tutorial.

Proposals should be submitted electronically by October 3rd to:

Professor Huan Liu
Department of Computer Science & Engineering
Ira A. Fulton School of Engineering
Arizona State University
Tempe, AZ 85287-8809
[email protected]

in PDF format (for other formats please contact the tutorial chair first). Proposals should include the following:

  • Basic information: Title, brief description, name and contact information for each tutor, length of the proposed tutorial. If the intended tutorial is expected to take longer than 2 hours a rationale is expected. Also identify any other venues in which the tutorial has been or will be presented.
  • Audience: Proposals must clearly identify the intended audience for the tutorial (e.g., novice,intermediate,expert).
    • What background will be required of the audience?
    • Why is this topic important/interesting to the SIAM data mining community?
    • What is the benefit to participants?
    • Provide some informal evidence that people would attend (e.g., related workshops).
  • Coverage: Enough material should be included to provide a sense of both the scope of material to be covered and the depth to which it will be covered. The more details that can be provided, the better (up to and including links to the actual slides or viewgraphs). Note that the tutors should not focus mainly on their own research results. If, for certain parts of the tutorial, the material comes directly from the tutors' own research or product, please indicate this clearly in the proposal.
  • Bios: Provide brief biographical information on each tutor (including qualifications with respect to the tutorial's topic).
  • Special equipment (if any): Please indicate any additional equipment needed (if any). The standard equipment includes an LCD projector, an overhead projector, a single projection screen and microphones.


* Submission by: October 3rd, 2005
* Decision Notification by: November 11, 2005
* Complete Set of Tutorial Viewgraphs (Slides) by: February 11, 2006


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