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.