This article proposes a semi-interactive system for visual data exploration using an iterative clustering that combines an automatic approach with an interactive one. We propose a framework to improve the interactivity between the user and the data analysis process, allowing him or her to participate actively in the iterative clustering tasks using a two-dimensional projection. Defining a cluster by its seed (center) and its limit, the proposed approach allows the user to modify the automated values or to define new seeds and the associated cluster limit himself or herself. The user can perform the clustering according to his or her visual perception manually and can also choose to let the automated approach find optimal seeds and then interact with the process to iterate the clustering process according to his or her visual perception and domain knowledge. Most of the evaluation criteria for clustering evaluate the complete clustering and not each cluster separately. In this article, we propose to adapt evaluation criteria to single clusters, allowing the users to evaluate their own clusters and perform the clustering iteratively until satisfaction. To evaluate our proposed approach, we conduct a user evaluation, where the users are asked to perform clustering interactively according to their visual perception and with the semi-interactive one. We also compare the obtained results with those of automated clustering. The quantitative results have shown that the cooperative approach can improve the clustering results in terms of accuracy.

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Semantic wikis enable collaboration between human agents for creating knowledge systems. In this way, data embedded in semantic wikis can be mined and the resulting knowledge patterns can be reused to extend and improve the structure of wikis. This paper proposes a method for guiding the reengineering and improving the structure of a semantic wiki. This method suggests the creation of categories and relations between categories using Formal Concept Analysis (FCA) and Relational Concept Analysis (RCA). FCA allows the design of a concept lattice while RCA provides relational attributes completing the content of formal concepts. The originality of the approach is to consider the wiki content from FCA and RCA points of view and to extract knowledge units from this content allowing a factorization and a reengineering of the wiki structure. This method is general and does not depend on any domain and can be generalized to every kind of semantic wiki. Examples are studied throughout the paper and experiments show the substantial results.