International Conference on Formal Concept Analysis (ICFCA 2010)
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Invited Speakers

Professor Vasu V.S.Alagar Professor Vasu V.S. Alagar, Concordia University, Canada
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Title: The Role of Concept, Context, and Component for Dependable Software Development.
Abstract: Software that impact our lives are embedded in the environment in which we act and hence our security and safety are dependent on its flawless functioning. An assessment of dependability of such embedded software systems includes an assessment of the process to develop the system and the system’s observable properties. Dependability criteria depend on the domain in which the software system is to serve. Thus, it should be formulated from domain concepts. Concepts in the domain should be analyzed to construct components. A component of the system may function as expected in one context of application and may fail to function as expected in another context. The system is dependable if the services resulting from every interaction between system components satisfy the dependability criteria in every context of operation. This paper explores the roles of concept analysis and context in determining dependability criteria at the domain level, the role of domain models in an automatic derivation of components and component-based systems, and the integrated role of context and components in the construction of context-aware and service-oriented systems.

Professor B.Ganter Professor Bernhard Ganter, Technische Universität Dresden, Germany
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Title: Four results on concept lattices that you probably did not think of.

Professor Kusnetsov Professor Sergei O. Kuznetsov, State University Higher School of Economics, Department of Applied Mathematics and Information Science, Moscow, Russia
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Relational Learning with FCA.
Abstract: For data given by binary object-attribute datatables Formal Concept Analysis (FCA) provides with convenient means for computing hierarchies of object classes and various types of dependencies between sets of attributes. In case of data different from standard contexts, to apply FCA techniques, one needs scaling data. Pattern structures propose a direct way of processing complex data such as graphs, numerical intervals and other. As compared to scaling, this way is more efficient from the computational point of view and proposes much better visualization of results. We consider knowledge discovery with pattern structures in several applications like structure-activity relationship, gene expression analysis, and text mining based on matching of syntactic trees. We give an overview of similar lattice-based approaches to processing relational data and relate the models that employ graph pattern structures to modern approaches in graph mining.

Professor Lhouari Nourine Professor Lhouari Nourine, Université Blaise Pascal, France
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Title: About the enumeration algorithms of closed sets.
Abstract: In this presentation we give a survey on advanced algorithms for FCA, in particular for the generation of closed sets and the stem base. We show that the enumeration problem of pseudo-intents is central to several themes and we give the recent results for the case of independent closure systems. We also present recent algorithms for generating closed sets.

Professor Gerd Stumme Professor Gerd Stumme, Universität Kassel, Germany
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Title: Chaos and Order in the Web 2.0
Abstract: Social bookmarking systems allow users to organize and share bookmarks, photos, etc. on the web. The reason for the success of these systems lies mainly in the fact that no specific skills are needed for publishing and editing. As these systems grow larger, however, the users feel the need for more structure for better organizing their resources. This poses new challenges for the research areas of information retrieval and knowledge discovery. The underlying data structure of social bookmarking systems are so-called folksonomies, which can be considered as triadic contexts, or alternatively as tri-partite hyper-graphs. We try to support users in bringing order into their web 2.0 data by exploiting how existing FCA and graph algorithms can be adapted to this new kind of structure. In the talk, I will first present our social bookmarking system BibSonomy , which supports the scientific community in organizing and sharing bookmarks and lists of publication references and which serves as a platform for our experiments with Information Retrieval and Knowledge Discovery algorithms. In the second part of the talk, I will present selected research results.

Professor Jean Vaillancourt Professor Jean Vaillancourt, Université du Québec en Outaouais, Canada
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Title: Statistical methods for data mining and knowledge discovery.
Abstract: Data mining for the purpose of knowledge discovery  has been around for decades and has enjoyed great interest and a flurry of research activity in just about every field within the scientific community. My purpose here is to broach the topic from a mathematician’s perspective with a view towards understanding what exactly can be said and concluded, when using some of the more sophisticated statistical tools to extract knowledge from large data bases.

The statistical techniques used currently in data mining practice often come from the great statistical toolbox built out of problem solving issues arising in other fields and taught in the standard science curriculum. They require several hypotheses to be checked and tested. These hypotheses reflect in large part the basic character of the data under study and it is vital to assess whether or not these hypotheses are valid in order to draw any inference from our data mining endeavor. This is especially true when using the more sophisticated and complex statistical estimators and tests.

In this talk  I first give a rapid bird’s eye view of the main statistical methods used in order to extract knowledge from databases comprising various types of observations. After touching briefly upon the matters of supervision, model selection and data regularization, the key issues of model assesment, selection and inference are perused. Finally, specific problems arising from applications around data mining and warehousing are delved in more deeply. In this last part, ten years of collaboration with experts in image retrieval, indexation and classification,  as well as in formal concept analysis, color my choice of examples and applications; however, the statistical methods discussed are, now as ever, universal.

Professor Rudolf Wille Professor Rudolf Wille (Honorary Speaker), Technische Universität Darmstadt, Germany
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Title: Mathematics, presenting, reflecting, judging.
Abstract: To understand what it means to present, to reflect, and to discuss mathematics,  mathematics shall in the first place be characterized in connection of Peirce's classification of the inquiring sciences. The threefold view of the universal categories of Peirce suggests to orientate the expositions for presenting on the self-image of mathematics as a First, for reflecting on the relationship of mathematics to the real world as a Second, and for judging on the sense, meaning and connection of mathematics as a Third. These expositions support the basic thesis: sense and meaning of mathematics finally lie in the fact, that mathematics may effectively support the rational communication of human beings.

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