Trong Dung Nguyen

Mining hepatitis data with temporal abstraction

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.

A visualization tool for interactive learning of large decision trees

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.

Visualization Support for User-Centered Model Selection in Knowledge Discovery and Data Mining

The problem of model selection in knowledge discovery and data mining—the selection of appropriate discovered patterns/models or algorithms to achieve such patterns/models—is generally a difficult task for the user as it requires meta-knowledge on algorithms/models and model performance metrics. Viewing knowledge discovery as a human-centered process that requires an effective collaboration between the user and the discovery system, our work aims to make model selection in knowledge discovery easier and more effective. For such a collaboration, our solution is to give the user the ability to try easily various alternatives and to compare competing models quantitatively and qualitatively. The basic idea of our solution is to integrate data and knowledge visualization with the knowledge discovery process in order to the support the participation of the user. We introduce the knowledge discovery system D2MS in which several visualization techniques of data and knowledge are developed and integrated into the steps of the knowledge discovery process. The visualizers in D2MS greatly help the user gain better insight in each step of the knowledge discovery process as well the relationship between data and discovered knowledge in the whole process.

Visualization support for a user-centered KDD process

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.

A Knowledge Discovery System with Support for Model Selection and Visualization

The process of knowledge discovery in databases consists of several steps that are iterative and interactive. In each application, to go through this process the user has to exploit different algorithms and their settings that usually yield multiple models. Model selection, that is, the selection of appropriate models or algorithms to achieve such models, requires meta-knowledge of algorithm/model and model performance metrics. Therefore, model selection is usually a difficult task for the user. We believe that simplifying the process of model selection for the user is crucial to the success of real-life knowledge discovery activities. As opposed to most related work that aims to automate model selection, in our view model selection is a semiautomatic process, 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 various alternatives and to compare competing models quantitatively by performance metrics, and qualitatively by effective visualization. This paper presents our research on model selection and visualization in the development of a knowledge discovery system called D2MS. The paper addresses the motivation of model selection in knowledge discovery and related work, gives an overview of D2MS, and describes its solution to model selection and visualization. It then presents the usefulness of D2MS model selection in two case studies of discovering medical knowledge in hospital data—on meningitis and stomach cancer—using three data mining methods of decision trees, conceptual clustering, and rule induction.

Visualization support for user-centered model selection in knowledge discovery in databases

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.

Data and Knowledge Visualization in Knowledge Discovery Process

The purpose of our work described in this paper is to develop and put a synergistic visualization of data and knowledge into the knowledge discovery process in order to support an active participation of the user. We introduce the knowledge discovery system D2MS in which several visualization techniques of data and knowledge are developed and integrated into the steps of the knowledge discovery process.

Interactive Visualization in Mining Large Decision Trees

This paper presents a tree visualizer that combines several techniques from the field of information visualization to handle efficiently large decision trees in an interactive mining system.

Induction of Decision Trees Based on the Rough Set Theory

This paper aimed at two following objectives. One was the introduction of a new measure (R-measure) of dependency between groups of attributes in a data set, inspired by the notion of dependency of attribute in the rough set theory. The second was the application of this measure to the problem of attribute selection in decision tree induction, and an experimental comparative evaluation of decision tree systems using R-measure and other different attribute selection measures most of them are widely used in machine learning: gain-ratio, gini-index, d N distance, relevance, x 2.

Visualization method and tool for interactive learning of large decision trees

When learning from large datasets, decision tree induction programs often produce very large trees. How to visualize efficiently trees in the learning process, particularly large trees, is still questionable and currently requires efficient tools. This paper presents a visualization method and tool for interactive learning of large decision trees, that includes a new visualization technique called T2.5D (stands for Tress 2.5 Dimensions). After a brief discussion on requirements for tree visualizers and related work, the paper focuses on presenting developing techniques for the issues (1) how to visualize efficiently large decision trees; and (2) how to visualize decision trees in the learning process.