Tuesday, March 23

High-Performance Data Mining

10:00 AM-12:00 PM
Room: Ballroom A

The current decade has seen an explosive growth in database technology and the amount of data collected. This has created an unprecedented opportunity for "data mining", which is a process of efficient supervised or unsupervised discovery of interesting information hidden in the data. Due to the huge size of data sets and the amount of computation involved in data mining algorithms, parallel processing is often considered an essential component for a successful data mining solution. The speakers in this minisymposium will discuss the state-of-the-art in high performance parallel formulations of computationally intensive kernels of commonly used data mining algorithms.

Organizer: Vipin Kumar
University of Minnesota, Minneapolis

10:00-10:25 Parallel Data Mining on Shared-Memory Multiprocessors
Rakesh Agarwal, Ching-Tien Ho, Leon Pauser and Mohammed Zaki, IBM Almaden Research Center
10:30-10:55 Dynamic Similarity: Mining Collections of Trajectories
Robert Grossman, University of Illinois, Chicago
11:00-11:25 Parallel Classification Algorithms for Out-of-Core Datasets
Khaled Alsabti, S. Mahesh Kumar, Organizer; and Sanjay Ranka, University of Florida
11:30-11:55 Parallel Algorithms for Mining Sequential Associations
Mahesh V. Joshi and George Karypis, University of Minnesota, Minneapolis; and Vipin Kumar, Organizer

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MMD, 11/16/98