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IEEE ICDM 2020 - Call for Workshop Papers (deadline Aug 24)
ICDM 2020: 20th IEEE International Conference on Data Mining
November 17-20, 2020, Sorrento, Italy
http://icdm2020.bigke.org/
The safety and well-being of all conference participants is our
priority.
We closely monitor the current COVID-19 situation. Depending on
the
situation, we will have the conference either as planned in
Sorrento, Italy
or as an online event. In any case, all deadlines and the
conference dates
remain the same. The Program Chairs and other organizers will post
more
information as soon as possible.
AIMS AND SCOPE
The IEEE International Conference on Data Mining (ICDM) has
established
itself as the world's premier research conference in data mining.
It
provides an international forum for presentation of original
research
results, as well as exchange and dissemination of innovative and
practical
development experiences. The conference covers all aspects of data
mining,
including algorithms, software, systems, and applications. ICDM
draws
researchers, application developers, and practitioners from a wide
range of
data mining related areas such as big data, deep learning, pattern
recognition, statistical and machine learning,databases, data
warehousing,
data visualization, knowledge-based systems, and high-performance
computing. By promoting novel, high-quality research findings, and
innovative solutions to challenging data mining problems, the
conference
seeks to advance the state-of-the-art in data mining.
ACCEPTED WORKSHOPS
SENTIRE: Sentiment Elicitation from Natural Text for Information
Retrieval
and Extraction - Erik Cambria
<cambria@ntu.edu.sg>
DMS: Data Mining for Service - Katsutoshi Yada
<kansai-u.ac.jp>
NeuRec: Advanced Neural Algorithms and Theories for Recommender
Systems -
Shoujin Wang
<shoujinwang@foxmail.com>
OEDM: Optimization Based Techniques for Emerging Data Mining - Shi
Yong <
yshi@ucas.ac.cn>
LITSA: Large-scale Industrial Time Series Analysis - Florent
Forest <
forest@lipn.univ-paris13.fr>
HDM: High Dimensional Data Mining - Ata Kaban
<A.Kaban@cs.bham.ac.uk>
TEAAM: Transparent, Explainable and Affective Data Mining in
Medical
Systems - Slawomir Nowaczyk
<Slawomir.Nowaczyk@hh.se>>
DL-IoT: Deep Learning for Internet of Things - Yunji Liang <
liangyunji@nwpu.edu.cn>
DDIF: Deep Data Intelligence for Finance - Fuxiang Chen
<cfuxiang@gmail.com>
CLEATED: Continual Learning and Adaptation for Time Evolving Data
- Yun
Sing
<y.koh@auckland.ac.nz>
DLC: Deep learning and clustering - Lazhar Labiod
<l.labiod@gmail.com>
DL-CTI: Deep Learning for Cyber Threat Intelligence - Hsinchun
Chen (Riley
McIsaac)
<hsinchun@email.arizona.edu>
MSDM: Multi-source data mining - Armelle Brun
<armelle.brun@loria.fr>
BDA: Blockchain Data Analytics - Cuneyt Gurcan Akcora <
cuneyt.akcora@umanitoba.ca>
MLCS: Multilingual Cognitive Services - Yihong Theis
<yihong@ksu.edu>
IMAGINE: Interpretable Machine leArninG models for bio and
chemoINformatics
and medicine - Rui Camacho
<rcamacho@fe.up.pt>
DMBIH: Data Mining in Biomedical Informatics and Healthcare -
Mohammad-Reza
Siadat
<siadat@oakland.edu>
UDML: Utility Driven Mining and Learning - Vincent S. Tseng <
vtseng@cs.nctu.edu.tw>
BigData4SmartEnergy: Big Data Analysis for Smart Energy - Ho-Jin
Choi <
hojinc@kaist.ac.kr>
DHA: Data Mining for Healthy Aging - Reynold CS
<ckcheng@cs.hku.hk>
MLLD: Mining and Learning in the Legal Domain - Shohreh
Shaghaghian <
shohreh.shaghaghian@thomsonreuters.com>
SSTDM: Spatial and Spatio-Temporal Data Mining - Ranga Raju
Vatsavai <
rrvatsav@ncsu.edu>
IncrLearn: Incremental classification and clustering, concept
drift,
novelty detection in big/fast data context - Jean-Charles Lamirel
<
lamirel@loria.fr>
DM-HT-D: Novel Data Mining Methods for the Analysis of
High-Throughput
Biological Data - Giuseppe Agapito
<agapito@unicz.it>
DMESS: Data Mining in Earth System Science - Forrest M. Hoffman
<
forrest@climatemodeling.org>
BSDM: Blockchain Systems for Decentralized Mining - Qiang Qu <
qiang@siat.ac.cn>
DLKT: Deep Learning for Knowledge Transfer - Fuzhen Zhuang <
zhuangfuzhen@ict.ac.cn>
PMC: Privacy Management in the Cyberspace - Mahmoud Barhamgi <
mahmoud.barhamgi@univ-lyon1.fr>
SUBMISSION GUIDELINES
Paper submissions should be limited to a maximum of eight (8)
pages, in the
IEEE 2-column format (
https://www.ieee.org/conferences/publishing/templates.html),
including the
bibliography and any possible appendices. Submissions longer than
8 pages
will be rejected without review. All submissions will be
triple-blind
reviewed by the Program Committee on the basis of technical
quality,
relevance to scope of the conference, originality, significance,
and
clarity. The following sections give further information for
authors.
Triple blind submission guidelines
Since 2011, ICDM has imposed a triple blind submission and review
policy
for all submissions. Authors must hence not use identifying
information in
the text of the paper and bibliographies must be referenced to
preserve
anonymity. Any papers available on the Web (including Arxiv) no
longer
qualify for ICDM submissions, as their author information is
already public.
What is triple blind reviewing?
The traditional blind paper submission hides the referee names
from the
authors, and the double-blind paper submission also hides the
author names
from the referees. The triple-blind reviewing further hides the
referee
names among referees during paper discussions before their
acceptance
decisions. The names of authors and referees remain known only to
the PC
Co-Chairs, and the author names are disclosed only after the
ranking and
acceptance of submissions are finalized. It is imperative that all
authors
of ICDM submissions conceal their identity and affiliation
information in
their paper submissions. It does not suffice to simply remove the
author
names and affiliations from the first page, but also in the
content of each
paper submission.
How to prepare your submissions
The authors shall omit their names from the submission. For
formatting
templates with author and institution information, simply replace
all these
information items in the template by "Anonymous".
In the submission, the authors should refer to their own prior
work like
the prior work of any other author, and include all relevant
citations.
This can be done either by referring to their prior work in the
third
person or referencing papers generically. For example, if your
name is
Smith and you have worked on clustering, instead of saying "We
extend our
earlier work on distance-based clustering (Smith 2005)," you might
say "We
extend Smith's earlier work (Smith 2005) on distance-based
clustering." The
authors shall exclude citations to their own work which is not
fundamental
to understanding the paper, including prior versions (e.g.,
technical
reports, unpublished internal documents) of the submitted paper.
Hence, do
not write: "In our previous work [3]" as it reveals that citation
3 is
written by the current authors. The authors shall remove mention
of funding
sources, personal acknowledgments, and other such auxiliary
information
that could be related to their identities. These can be
reinstituted in the
camera-ready copy once the paper is accepted for publication. The
authors
shall make statements on well-known or unique systems that
identify an
author, as vague in respect to identifying the authors as
possible. The
submitted files shall be named with care to ensure that author
anonymity is
not compromised by the file names. For example, do not name your
submission
"Smith.pdf", instead give it a name that is descriptive of the
title of
your paper, such as "ANewApproachtoClustering.pdf" (or a shorter
version of
the same).
Algorithms and resources used in a paper should be described as
completely
as possible to allow reproducibility. This includes experimental
methodology, empirical evaluations, and results. Authors are
strongly
encouraged to make their code and data publicly available whenever
possible. In addition, authors are strongly encouraged to also
report,
whenever possible, results for their methods on publicly available
datasets.
Accepted papers will be published in the conference proceedings by
the IEEE
Computer Society Press. All manuscripts are submitted as full
papers and
are reviewed based on their scientific merit. There is no separate
abstract
submission step. There are no separate industrial, application,
short paper
or poster tracks during submission. Manuscripts must be submitted
electronically in online submission system (
http://wi-lab.com/cyberchair/2020/icdm20/scripts/ws_submit.php?subarea=S).
We do not accept email submissions.
ATTENDANCE
ICDM is a premier forum for presenting and discussing current
research in
data mining. Therefore, at least one author of each accepted paper
must
complete the conference registration and present the paper at the
conference, in order for the paper to be included in the
proceedings and
conference program.
All deadlines are at 11:59PM Pacific Daylight Time.
* Workshop paper submissions: August 24, 2020
* Workshop paper notification: September 17, 2020
* Camera-ready deadline and copyright forms: September 24, 2020
* Conference dates: November 17-20, 2020
WORKSHOP CHAIRS
* Giuseppe Di Fatta, University of Reading, UK
* Victor Sheng, Texas Tech University, USA
PUBLICITY CHAIRS
* Ting Bai, Beijing University of Posts and Telecommunications,
China
* Carson K. Leung, University of Manitoba, Canada
* Washio Takashi, Osaka University, Japan
MORE INFORMATION
More information about ICDM 2020:
http://icdm2020.bigke.org/
Email:
icdm2020chairs@gamil.com
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