IATVI 2012 - The International Special Session on Intelligent Analysis of Time Varying Information and Concept Drift Management

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Category IATVI 2012

Deadline: December 12, 2011 | Date: June 09, 2012

Venue/Country: Dalian, China

Updated: 2012-01-02 20:39:30 (GMT+9)

Call For Papers - CFP

[Session Chairs]: Jean-Charles Lamirel (lamirelloria.fr), Pascal Cuxac (pascal.cuxacinist.fr)

[Scope]: 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 and adaptive information retrieval, user behaviour analysis and recommendation systems, technological and scientific survey, anomaly or intrusion detection, and even genomic information analysis, in bioinformatics. The term 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 familiy of these methods, were initiated incremental variants making it possible to take into account the temporal component of a data flow. In a more general way incremental clustering algorithms and novelty detection approaches are subjected to the following constraints: (1) Possibility to be applied without knowing as a preliminary all the data to be analyzed; (2) Taking into account of a new data must be carried out without making intensive use of the already considered data; (3) Result must but available after insertion of all new data and must not depend on the order of arrival of the data; (4) Potentia change in the data des_cription space must be taken into consideration.

Topics: Incremental techniques:

Novelty detection algorithms and techniques

Concept drift detection and management techniques

Incremental clustering methods (hierarchical, density-based, ...)

Adaptive neural methods and associated Hebbian learning techniques

Probabilistic approaches

Graph partitioning methods and incremental clustering approaches based on attributed graphs

Incremental clustering approaches based on swarm intelligence and genetic algorithms

Visualization methods for evolving data analysis results

Application domain:

Evolving textual information analysis

Genomics and DNA micro-array data analysis

Ambient intelligence and robotics

Industrial process management and control

Privacy, security and biometrics

Intrusion and anomaly detection

Adaptive recommendation and filtering systems

Supervision of communication networks

Energy management and planning