Special Session on Large-Scale Data Mining
7th International Conference on High Performance Computing
December 17-20, 2000 --- Bangalore, India
One Microsoft Way
Redmond WA 98052
Mohammed J. Zaki
Computer Science Dept.
Rensselaer Polytechnic Institute
Troy, NY 12180
Large-Scale Data Mining
In the present times we are witnessing an explosive growth in the
amount of data that is being collected in the business and scientific arena.
Data warehouses are filling up with huge amounts of data in every conceivable
form. In most cases, the sheer size of the datasets prevents the vast majority
of the data from being deeply analyzed. Often large portions may not have
been examined at all. The field of Data Mining (or Knowledge Discovery
in Databases) attempts to develop automatic procedures that search these
enormous data sets to obtain useful information that would otherwise remain
undiscovered. Such new knowledge can take the form of patterns, rules,
clusters, or anomalies that exist in the massive datasets. These discoveries
could potentially be of great significance to scientific or business organizations.
Given the size and dimensionality of the datasets, high performance algorithms
and systems are an integral component of a successful data mining solution.
The objective of this special session is to bring together technologists
and researchers at the forefront of this exciting field to present and
discuss their state-of-the-art work. Authors are invited to submit original
unpublished manuscripts for the special session on Large Scale Data Mining.
Topics of interest include (but are not limited to):
Efficient, scalable, sequential or parallel and distributed algorithms
for various data mining techniques, such as
Studying the design of fast methods for the overall data mining process,
from the initial data selection to the extraction and management of discovered
Scalable data mining on heterogeneous data sources (e.g. the Web, sequence
data, images, video, etc).
Development of scalable data mining systems in e-commerce, retail, finance,
the sciences, etc.
A scalable approach to balanced, high-dimensional clustering of market
baskets, A. Strehl and J. Shosh, Univ. of Texas at Austin
Dynamic integration of decision committees, A. Tsymbal, Univ. of Jyvaskyla
Incremental mining of constrained associations, S. Thomas and S. Chakravarthy,
Univ. of Texas at Arlington
Scalable, disctibuted and dynamic mining of association rules, V.S. Ananthanarayana,
D.K. Subramanian, and M. NarasimhaMurty, Indian Institute of Science, Bangalore
Papers published as regular papers in the HiPC'00 Conference Proceedings
The paper must be clearly identified as submitted to "Large-Scale Data
Mining" session. Other submission guidelines are identical to the HiPC
guidelines. Papers are to be sent to the HiPC program Chair. The guidelines
are summarized here (for details, see www.hipc.org). Submit original research
papers not to exceed 15 double-spaced pages of text using 12-point size
type on 8.5 x 11 inch pages. Figures and Tables may use additional pages.
Preferably send your paper as a correct PostScript (level 2) file. Ensure
the PostScript prints on PostScript printers using 8.5x11 paper. In addition
to the PostScript, your Email must include, in ASCII form: title, author
name(s), abstract, postal address, e-mail address, and telephone and fax
numbers. Include "Large-Scale data Mining" in the ASCII header as well
as in the paper title page.
Send Electronic submissions to: email@example.com
Alternatively send 6 hard copies (by mail, not fax) to the Program Chair
at the address:
Dept. de Arquitectura de Computadores
Universidad Politecnica de Catalunya
c/ Jordi Girona 1-3, Modulo D6
08034 Barcelona, SPAIN
Papers Due: May 1st, 2000
Acceptance Notification: June 30th, 2000
Camera Ready Papers Due: August 15th, 2000
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