The hepatitis temporal database collected at Chiba university hospital between 1982--2001 was recently given to challenge the KDD research. The database is large where each patient corresponds to 983 tests represented as sequences of irregular timestamp points with different lengths. This paper presents a temporal abstraction approach to mining knowledge from this hepatitis database. Exploiting hepatitis background knowledge and data analysis, we introduce new notions and methods for abstracting short-term changed and long-term changed tests. The abstracted data allow us to apply different machine learning methods for finding knowledge part of which is considered as new and interesting by medical doctors.

Viewing knowledge discovery as a user-centered process that requires an effective collaboration between the user and the discovery system, our work aims to support an active role of the user in that process by developing synergistic visualization tools integrated in our discovery system D2MS. These tools provide an ability of visualizing the entire process of knowledge discovery in order to help the user with data preprocessing, selecting mining algorithms and parameters, evaluating and comparing discovered models, and taking control of the whole discover process. Our case-studies with two medical datasets on meningitis and stomach cancer show that, with visualization tools in D2MS, the user gains better insight in each step of the knowledge discovery process as well the relationship between data and discovered knowledge.

The process of knowledge discovery in databases inherently consists of several steps that are necessarily iterative and interactive. In each application, to go through this process the user has to exploit different algorithms and their settings that usually yield different discovered models. The selection of appropriate discovered models or algorithms to achieve such models, referred to as model selection-requires meta-knowledge on algorithm/model and model performance metrics - is generally a difficult task for the user. Taking account of this difficulty, we consider that the ease of model selection is crucial in the success of real-life knowledge discovery activities. Different from most related work that aims to an automatic model selection, in our view model selection should be a semiautomatic work requiring an effective collaboration between the user and the discovery system. For such a collaboration, our solution is to give the user the ability to try easily various alternatives and to compare competing models quantitatively by performance metrics, and qualitatively by effective visualization. This paper presents our research on such model selection and visualization in the development of a knowledge discovery system called D2MS.

Decision tree induction is certainly among the most applicable learning techniques due to its power and simplicity. However learning decision trees from large datasets, particularly in data mining, is quite different from learning from small or moderately sized datasets. When learning from large datasets, decision tree induction programs often produce very large trees. How to efficiently visualize trees in the learning process, particularly large trees, is still questionable and currently requires efficient tools. The paper presents a visualization tool for interactive learning of large decision trees, that includes a new visualization technique called T2.5D (Trees 2.5 Dimensions). After a brief discussion on requirements for tree visualizers and related work, the paper focuses on presenting developing techniques for two issues: (1) how to visualize efficiently large decision trees; and (2) how to visualize decision trees in the learning process.

Robotic peg-in-hole assembly is a very complex dynamic process. A complete analysis of the geometric and dynamic equilibrium conditions has established a general assembly model for a certain goal: to understand the mechanism of dynamic insertion and the development of compliant devices capable of avoiding wedging and jamming in high speed assembly operation. This paper presents a dynamic model which includes the different factors influencing the behaviour of assembly processes such as: gravity, inertia, dry friction, compliance and insertion speed. The simulation of proposed model allows us to choose the optimal parameters of dynamic compliance device. An application of this work is presented as a development of compliance device for high speed and precision chamferless assembly.