Call for Papers - Special Issue on Mining Streaming Data
Information
Sciences Journal - Informatics and Computer Science Intelligent
Systems Applications
An International Journal, Elsevier
Guest Editors: Jianping Zhang, Huan Liu, and Paul P. Wang
Data mining is increasingly recognized as a key technique to analyzing
and understanding the
flood of digital data in many organizations. Data can grow without
limit at a high rate of
millions of data items per day. Domains with these continuous data
streams include credit
fraud detection, mining e-commerce data, web mining, stock analysis,
network intrusion detection,
telecommunication data mining, and counter-terrorism data mining.
Mining data streams brings
unique opportunities but also new challenges. The main challenge is
that `data-intensive' mining is
constrained by limited resources of time, memory, and sample size. Data
mining has traditionally
been performed over static datasets, where data mining algorithms can
afford to read the input data
several times. When the source of data items is an open-ended data
stream, not all data can be
loaded into the memory and off-line mining with a fixed size dataset is
no longer technically feasible
due to the unique features of streaming data.
This special issue is dedicated to Mining Streaming Data. We solicit
high-quality, original papers. Papers
should address issues related to mining streaming data, including but
not
limited to the following topics:
- Incremental learning and concept drift
- Mining skewed or unbalanced data
- Data reduction
- Scaling data mining algorithms
- Change detection
- Combining labeled and unlabeled data
- Active learning and semi-supervised learning
Submissions should be in 12pt font, 1.5 line-spacing, and should not
exceed 25 pages.
Submission deadline is March 1, 2004. Papers in PDF format can
be sent to
jzhang@mitre.org or hliu@asu.edu by email. If electronic submission is
not possible, please
send five hard copies to the following addresses:
Jianping Zhang
The MITRE Corporation, M/S H305
7515 Colshire Drive
McLean, Virginia 22102-7508
USA
or
Huan Liu
Department of Computer Science & Engineering
Tempe, AZ 85287-8809
USA