Djamel A. Zighed

Principles of Data Mining and Knowledge Discovery

Hidden Markov Models (HMM) have proven to be useful in a variety of real world applications where considerations for uncertainty are crucial. Such an advantage can be more leveraged if HMM can be scaled up to deal with complex problems. In this paper, we introduce, analyze and demonstrate SelfSimilar Layered HMM (SSLHMM), for a certain group of complex problems which show self-similar property, and exploit this property to reduce the complexity of model construction. We show how the embedded knowledge of selfsimilar structure can be used to reduce the complexity of learning and increase the accuracy of the learned model. Moreover, we introduce three different types of self-similarity in SSLHMM, and investigate their performance in the context of synthetic data and real-world network databases. We show that SSLHMM has several advantages comparing to conventional HMM techniques and it is more efficient and accurate than one-step, flat method for model construction.

Information diffusion in online social networks: a survey

Online social networks play a major role in the spread of information at very large scale. A lot of effort have been made in order to understand this phenomenon, ranging from popular topic detection to information diffusion modeling, including influential spreaders identification. In this article, we present a survey of representative methods dealing with these issues and propose a taxonomy that summarizes the state-of-the-art. The objective is to provide a comprehensive analysis and guide of existing efforts around information diffusion in social networks. This survey is intended to help researchers in quickly understanding existing works and possible improvements to bring.

FUSINTER: a method for discretization of continuous attributes

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.

Roles in social networks: Methodologies and research issues

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 roles which can be characterized as positions, behaviors, or virtual identities. These roles may be developed in social networks, and they keep changing and evolving over time. In this article, a survey of the state-of-the-art approaches is presented regarding the identification of roles within the context of a social network. It is shown that social roles exist as a function of each other; they appear and evolve through user interaction. Different approaches are analyzed and additional characteristics that should be taken into account during the role analysis are discussed.

Advances in Knowledge Discovery and Management

During the last decade, the French-speaking scientific community developed a very strong research activity in the field of Knowledge Discovery and Management (KDM or EGC for Extraction et Gestion des Connaissances in French), which is concerned with, among others, Data Mining, Knowledge Discovery, Business Intelligence, Knowledge Engineering and SemanticWeb. The recent and novel research contributions collected in this book are extended and reworked versions of a selection of the best papers that were originally presented in French at the EGC 2009 Conference held in Strasbourg, France on January 2009. The volume is organized in four parts. Part I includes five papers concerned by various aspects of supervised learning or information retrieval. Part II presents five papers concerned with unsupervised learning issues. Part III includes two papers on data streaming and two on security while in Part IV the last four papers are concerned with ontologies and semantic.

Improving Classification by Removing or Relabeling Mislabeled Instances

It is common that a database contains noisy data. An important source of noise consists in mislabeled training instances. We present a new approach that deals with improving classification accuracies in such a case by using a preliminary filtering procedure. An example is suspect when in its neighborhood defined by a geometrical graph the proportion of examples of the same class is not significantly greater than in the whole database. Such suspect examples in the training data can be removed or relabeled. The filtered training set is then provided as input to learning algorithm. Our experiments on ten benchmarks of UCI Machine Learning Repository using 1-NN as the final algorithm show that removing give better results than relabeling. Removing allows maintaining the generalization error rate when we introduce from 0 to 20% of noise on the class, especially when classes are well separable.

SONDY: an open source platform for social dynamics mining and analysis

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.

Extracting Social Networks to Understand Interaction

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.

Goodness-of-Fit Measures for Induction Trees

This paper is concerned with the goodness-of-fit of induced decision trees. Namely, we explore the possibility to measure the goodness-of-fit as it is classically done in statistical modeling. We show how Chi-square statistics and especially the Log-likelihood Ratio statistic that is abundantly used in the modeling of cross tables, can be adapted for induction trees. The Log-likelihood Ratio is well suited for testing the significance of the difference between two nested trees. In addition, we derive from it pseudo R 2’s. We propose also adapted forms of the Akaike (AIC) and Bayesian (BIC) information criteria that prove useful in selecting the best compromise model between fit and complexity.

Topological representation model for image database query

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.