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)
Website: https://sites.google.com/view/incrlearn

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:

Paper submissions should be limited to a maximum of ten (10) pages, in the IEEE 2-column format
(https://www.ieee.org/conferences/publishing/templates.html), including the bibliography and any possible appendices.

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.


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.


For any additional info, please email to:
* Pascal Cuxac - [log in to unmask]
* Jean-Charles Lamirel - [log in to unmask]
* Mustapha Lebbah - [log in to unmask]paris13.fr​

Pascal Cuxac

Responsable Service Text & Data Mining

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