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IncrLearn 2020 : IncrLearn Workshop: Incremental classification and clustering, concept drift, novelty detection in big/fast data context

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Link: https://sites.google.com/view/incrlearn
 
When Nov 17, 2020 - Nov 20, 2020
Where Sorrento - Italia
Submission Deadline Aug 24, 2020
Notification Due Sep 17, 2020
Final Version Due Sep 24, 2020
Categories    clustering   classification   machine learning   incremental learning
 

Call For Papers

First Call For Papers
IncrLearn Workshop: Incremental classification and clustering,
concept drift, novelty detection in big/fast data context

In conjunction with: 20th IEEE International Conference on Data Mining (ICDM 2020)
Workshop Website: https://sites.google.com/view/incrlearn
ICDM 2020 Website: http://icdm2020.bigke.org

The development of dynamic information analysis methods, like incremental
classification/clustering, concept drift management and novelty detection
techniques, is becoming a central concern in a bunch of applications whose main
goal is to deal with information which is varying over time or with information
flows that can oversize memory storage or computation capacity. These applications
relate themselves to very various and highly strategic domains, including
web mining, social network analysis, adaptive information retrieval, anomaly or
intrusion detection, process control and management recommender systems,
technological and scientific survey, and even genomic information analysis,
in bioinformatics.

The term “incremental” is often associated to the terms evolutionary, adaptive,
interactive, on-line, or batch. The majority of the learning methods were
initially defined in a non-incremental way. However, in each of these families,
were initiated incremental methods making it possible to take into account the
temporal component of a data flow or to achieve learning on huge/fast datasets
in a tractable way. In a more general way incremental classification/clustering
algorithms and novelty detection approaches are subjected to the following
constraints:

* Potential changes in the data description space must be taken into consideration;
* Possibility to be applied without knowing as a preliminary all the data
to be analyzed;
* Taking into account of a new data must be carried out without making intensive
use of the already considered data;
* Result must but available after insertion of all new data.

The above mentioned constraints clearly follow the VVV (Volume-Velocity and
Variety) rule and thus directly fit with big/fast data context. This workshop aims
to offer a meeting opportunity for academics and industry-related researchers,
belonging to the various communities of Computational Intelligence, Machine
Learning, Experimental Design, Data Mining and Big/Fast Data Management to discuss
new areas of incremental classification, concept drift management and novelty
detection and on their application to analysis of time varying information and huge
dataset of various natures. Another important aim of the workshop is to bridge the
gap between data acquisition or experimentation and model building.

Through an exhaustive coverage of the incremental learning area workshop will
provide fruitful exchanges between plenaries, contributors and workshop attendees.
The emerging big/fast data context will be taken into consideration in the workshop.
The set of proposed incremental techniques includes, but is not limited to:

* Novelty detection algorithms and techniques
* Semi-supervised and active learning approaches
* Machine learning for data streams
* Adaptive hierarchical, k-means or density-based methods
* Adaptive neural methods and associated Hebbian learning techniques
* Incremental deep learning
* Multiview diachronic approaches
* Probabilistic approaches
* Distributed approaches
* Graph partitioning methods and incremental clustering approaches based on
attributed graphs
* Incremental clustering approaches based on swarm intelligence and genetic
algorithms
* Evolving classifier ensemble techniques
* Incremental classification methods and incremental classifier evaluation
* Dynamic feature selection techniques
* Clustering of time series
* Visualization methods for evolving data analysis results

The list of application domain is includes, but it is not limited to:

* Evolving textual information analysis
* Evolving social network analysis
* Dynamic process control and tracking
* Intrusion and anomaly detection
* Genomics and DNA microarray data analysis
* Adaptive recommender and filtering systems
* Scientometrics, webometrics and technological survey


Important dates:
* Paper submission: August 24, 2020
* Notification of acceptance: September 17, 2020
* Camera-ready (+ copyright): September 24, 2020
* IncrLearn workshop: November 17, 2020
* ICDM 2020 conference: November 17-20, 2020

Submission instructions:
The objective of this workshop is to facilitate presentations and discussions to
share experience and knowledge on the issues related to incremental learning.

Different kinds of submissions are welcome:
* Academic contributions related to theoretical research
* Contributions on the practical relevance of research work or models

Submission format:
The workshop will accept short as well long submissions:
Short submissions will be more focused on on-going works and should have
four (4) pages, Long submissions will concern more advanced works and should
have at least five (5) pages and be limited to a maximum of eight (8) pages.

Both types of submissions must be presented in the IEEE 2-column format
(https://www.ieee.org/conferences/publishing/templates.html), including
the bibliography and any possible appendices.

Manuscripts must be submitted electronically in ICDM 2020 online submission
system (http://wi-lab.com/cyberchair/2020/icdm20/scripts/submit.php?subarea=DM).

Reviewing will be triple blind. 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. It is imperative that all authors of 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.

Publication:
All accepted workshop papers will be published in ICDM Workshop Proceedings
available at the conference time. After the workshop, if the quantity and quality
of submissions justifies a special journal issue, authors of selected papers will
be invited to re-submit their work to be considered for inclusion in a special
issue of a journal.

Contacts:
For any additional info, please email to:
* Pascal Cuxac - pascal.cuxac@inist.fr
* Jean-Charles Lamirel - lamirel@loria.fr
* Mustapha Lebbah - mustapha.lebbah@lipn.univ-paris13.fr

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