INCREMENTAL CLASSIFICATION, CONCEPT DRIFT AND NOVELTY DETECTION
*** (Extended deadline August 17th 2013) ***
In conjunction with
INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2013, Dallas, Texas / December 7-11, 2013) [http://icdm2013.rutgers.edu/]
The development of dynamic information analysis methods, like incremental 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. 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 dynamics, 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 datastream. In a more general way incremental clustering algorithms and novelty detection approaches are subjected to the following constraints: - 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; - Potential changes in the data description space must be taken into consideration; - Independency of order of data arrival.
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 and Data Mining to discuss new areas of incremental clustering, concept drift management and novelty detection and on their application to analysis of time varying information of various natures. Another important aim of the workshop is to bridge the gap between data acquisition or experimentation and model building.
The set of proposed incremental techniques includes, but is not limited to:
- Novelty and drift detection algorithms and techniques - Adaptive hierarchical, k-means or density based methods - Adaptive neural methods and associated Hebbian learning techniques - Multiview diachronic approaches - Probabilistic approaches like LDA or ICA-based 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 - Dynamic features selection techniques - Object tracking techniques - 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 - Dynamic scene analysis - Intrusion and anomaly detection - Genomics and DNA microarray data analysis - Adaptive recommender and filtering systems - Scientometrics, webometrics and technological survey
All accepted workshop papers will be published in formal proceedings by the IEEE Computer Society Press.
Invited speaker: Zhi-Hua Zhou, Nanjing University, China
- Paper submission: August 17, 2013 (Extended) - Notification of acceptance: September 24, 2013 - Camera-ready: October 15, 2013 - ICDM 2013 Conference: December 7, 2013
Important - Submission Guidelines:
- Please follow the regular submission guidelines of ICDM 2013 (paper submissions should be limited to a maximum of *8* pages) http://icdm2013.rutgers.edu/author-instructions - and use this link to submit your paper (IclaNov has the number 7 in the page): http://wi-lab.com/cyberchair/2013/icdm13/scripts/ws_submit.php
pascal.cuxac at inist.fr – jean-charles.lamirel at loria.fr - vincent.lemaire at orange.com,
Abou-Nasr Mahmoud Ford Motor Company USA Al Shehabi Shadi Allepo University Syria Albatineh Ahmed Dept of Biostatistics Florida Int. U. Miami USA Alippi Cesare Politecnico di Milano Italia Arredondo Tomas U.T.F.S.M. Valparaíso Chile Bennani Younes LIPN, Paris France Bifet Albert University of Waikato, Hamilton New Zealand Bondu Alexis EDF R&D France Cabanes Guenael LIPN, Paris France Chawla Nitesh Notre Dame University, Indiana USA Chen Chaomei Drexel University, Philadelphia USA Cuxac Pascal INIST-CNRS, Nancy France Diallo Abdoulaye B. UQAM Montreal Canada El Haddadi Anass IRIT, Toulouse France Escalante Hugo Jair National Institute of Astrophysics Optics and Electronics Mexico García-Rodríguez José University of Alicante Spain Glanzel Wolfgang KU Leuven, Leuven Belgia Hammer Barbara University of Bielefeld Germany Kumova Bora I. Izmir University Turkey Kuntz-Cosperec Pascale Polytech'Nantes France Lallich Stephane University of Lyon 2 France Lamirel Jean-Charles TALARIS- LORIA, Nancy France Lebbah Mustapha LIPN, Paris France Lemaire Vincent Orange Labs, Lannion France Lenca Philippe Telecom Bretagne France Li Bin UTS, Sydney Australia Nuggent Rebecca Carnegie Mellon University, Pittsburgh USA Popescu Florin Fraunhofer Institute, Berlin Germany Roveri Manuel Politecnico di Milano Italia Tamir Dan Texas State University, San Marcos USA Torre Fabien University of Lille 3 France Zhou Zhi-Hua Nanjing University China Zhu Xingquan UTS, Sydney Australia
Dr habil. Jean-Charles LAMIREL Maître de Conférences, Habilité à Diriger des Recherches Université de Strasbourg Projet INRIA TALARIS - LORIA - Nancy GSM : 0624365491
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