IClaNov 2014 - Incremental Classification, concept drift and Novelty detection (IClaNov)

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 data stream. 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.
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
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 micro-array data analysis
Adaptive recommender and filtering systems
Scientometrics, webometrics and technological survey