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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)
In this paper, we apply our PrototypeLines to Chronic Hepatitis Data and demonstrate the results so that domain experts can inspect them closely. PrototypeLines represents a method which visualizes irregular multi-dimensional time-series data as a sequence of probabilistic prototypes. It displays summarized information based on a proba-bilistic mixture(More)
This paper proposes a novel decision tree for a data set with time-series attributes. Our time-series tree has a value (i.e. a time sequence) of a time-series attribute in its internal node, and splits examples based on dis-similarity between a pair of time sequences. Our method selects, for a split test, a time sequence which exists in data by exhaustive(More)
A machine learning technique called Graph-Based Induction (GBI) efficiently extracts typical patterns from graph-structured data by stepwise pair expansion (pairwise chunking). It is very efficient because of its greedy search. Meanwhile, a decision tree is an effective means of data classification from which rules that are easy to understand can be(More)
Various data mining methods have been developed last few years for hepatitis study using a large temporal and relational database given to the research community. In this work we introduce a novel temporal abstraction method to this study by detecting and exploiting temporal patterns and relations between events in viral hepatitis such as " event A slightly(More)
More than 10 years ago Haux et al. tried to answer the question how health care provision will look like in the year 2013. A follow-up workshop was held in Braunschweig, Germany, for 2 days in May, 2013, with 20 invited international experts in biomedical and health informatics. Among other things it had the objectives to discuss the suggested goals and(More)
We analyzed the hepatitis data by Decision Tree Graph-Based Induction (DT-GBI), which constructs a decision tree for graph-structured data while simultaneously constructing attributes for classification. An attribute at each node in the decision tree is a discriminative pattern (subgraph) in the input graph, and extracted by Graph-Based Induction (GBI). We(More)