This paper describes SONDY, a tool for analysis of trends and dynamics in online social network data. SONDY addresses two audiences: (i) end-users who want to explore social activity and (ii) researchers who want to experiment and compare mining techniques on social data. SONDY helps end-users like media analysts or journalists understand social network users interests and activity by providing emerging topics and events detection as well as network analysis functionalities. To this end, the application proposes visualizations such as interactive time-lines that summarize information and colored user graphs that reflect the structure of the network. SONDY also provides researchers an easy way to compare and evaluate recent techniques to mine social data, implement new algorithms and extend the application without being concerned with how to make it accessible. In the demo, participants will be invited to explore information from several datasets of various sizes and origins (such as a dataset consisting of 7,874,772 messages published by 1,697,759 Twitter users during a period of 7 days) and apply the different functionalities of the platform in real-time.

Online discussions became increasingly widespread with the Web 2.0: no matter the distance, whether you know the person or not, you can discuss and exchange ideas with people all over the world through forums, blogs, and newsgroups. The news websites have extensively used forums in order to encourage the reader being a real participant in the information media. This paper aims at automatically extracting the celebrities from such discussions. We propose certain meta-criteria and we provide an evaluation on a dataset of 35,175 posts written by 14,443 users. The results show that one of the proposed meta-criteria succeeds in extracting celebrities and allows for further improvements.

The expansion of web user roles is, nowadays, a fact due to the ability of users to interact, discuss, exchange ideas and opinions, and form social networks through the web. The interaction level among users leads to the appearance of several social

Web forums are a huge data source. They allow people to interact with unknown individuals. Studying forums shows that the interaction is not obvious only through the structure but also through the content of the post. Taking into account this observation, we extract a social network with different kinds of relationships i.e. the structural relation, the name and the text quotations relation. We present here the promising results we obtain, and the difficulties we face while extracting the quotations in this kind of textual content. These results are obtained from real data (from two information websites) which make the validation difficult. So, we create a validation protocol composed of two steps and based on human raters. Finally, we will see the objective of this work which is understanding interactions in order to extract the social roles of individuals.

This paper presents a step in a long process of analyzing, structuring, and retrieving multimedia databases. Indeed, we propose to bring an improvement to an existing content based image retrieval approach. We propose an effective method for locally updating neighborhood graphs which constitute our multimedia index. This method is based on an intelligent way for locating points in a multidimensional space. Promising results are obtained after experimentations on various databases. Future issues of the proposed approach are very relevant

Decision tree methods generally suppose that the number of categories of the attribute to be predicted is fixed. Breiman et al., with their Twoing criterion in CART, considered gathering the categories of the predicted attribute into two supermodalities. In this article, we propose an extension of this method. We try to merge the categories in an optimal unspecified number of supermodalities. Our method, called Arbogodaï, allows during tree growing for grouping categories of the target variable as well as categories of the predictive attributes. It handles both categorical and quantitative attributes. At the end, the user can choose to generate either a set of single rules or a set of multiconclusion rules that provide interval-like predictions. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 693–718, 2005.

Search algorithms in image databases usually return k nearest neighbours (kNN) of an image according to a similarity measure. This approach presents some anomalies and is based on assumptions that are not always satisfied. We have examined the causes of these anomalies and we have concluded that image query models have to exploit topological properties rather than the similarity degree. This paper proposes a topological model based on neighbourhood graphs built on automatically extracted image features. Each image is represented as a feature vector in R p and stands for a node in the neighbourhood graph. The graph exploration corresponds to database browsing, the neighbours of a node represent similar images. In order to perform query by example, the query image is represented as a R p feature vector and inserted in the graph by locally updating the neighbourhood graph. The topology of an image database is more informative than a similarity measure usually applied in content based image retrieval, as proved by our experiments. A prototype of a visualization and query tool called Smart Image Query (SIQ) is also introduced.

In induction graphs methods such as C4.51 or SIPINA2, taking continuous attributes into account needs particular discretization procedures. In this paper, we propose on the one hand, an axiomatic leading to a set of criteria which can be used for continuous attributes discretization, and on the other hand, a method of discretization called FUSINTER. The results obtained by FUSINTER are compared to those obtained by techniques developed by Fayyad and Irani3 and Kerber4 and they have proved better for the majority of the examples studied.