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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(More)
While chances are viewed in the chance discovery research context as events/situations with significant impact on human decision making, we are interested in a subset of those chances that are unexpected or contradictory with human common knowledge. In our view, the human role is essential in chance discovery, and there is a need for chance discovery(More)
The high generalization ability of support vector machines (SVMs) has been shown in many practical applications, however, they are considerably slower in test phase than other learning approaches due to the possibly big number of support vectors comprised in their solution. In this letter, we describe a method to reduce such number of support vectors. The(More)
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(More)
The hepatitis database contains the results of laboratory examinations taken on the patients of hepatitis B and C during 1982-2001, and recently was given to challenge data mining research. This paper presents our approach to two problems of distinguishing hepatitis B and C, and the relations between laboratory data and fibrosis stages. The approach is(More)