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Temporal abstraction has been known as a powerful approach of data abstraction by converting temporal data into interval with abstracted values including trends and states. Most temporal abstraction methods, however, has been developed for regular temporal data, and they cannot be used when temporal data are collected irregularly. In this paper we introduced a temporal abstraction approach to irregular temporal data inspired from a real-life application of a large database in hepatitis domain.

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.

Decision tree learning has become a popular and practical method in data mining because of its high predictive accuracy and ease of use. However, a set of if-then rules generated from large trees may be preferred in many cases because of at least three reasons: (i) large decision trees are difficult to understand as we may not see their hierarchical structure or get lost in navigating them, (ii) the tree structure may cause individual subconcepts to be fragmented (this is sometimes known as the "replicated subtree" problem), (iii) it is easier to combine new discovered rules with existing knowledge in a given domain. To fulfill that need, the popular decision tree learning system C4.5 applies a rule post-pruning algorithm to transform a decision tree into a rule set. However, by using a global optimization strategy, C4.5rules functions extremely slow on large datasets. On the other hand, rule post-pruning algorithms that learn a set of rules by the separate-and-conquer strategy such as CN2, IREP, or RIPPER can be scalable to large datasets, but they suffer from the crucial problem of overpruning, and do not often achieve a high accuracy as C4.5. This paper proposes a scalable algorithm for rule post-pruning of large decision trees that employs incremental pruning with improvements in order to overcome the overpruning problem. Experiments show that the new algorithm can produce rule sets that are as accurate as those generated by C4.5 and is scalable for large datasets.

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.

In this paper we describe system CABRO for decision tree induction (DTI) that contributes to the combination of machine learning, visualisation and model selection techniques. We first discuss some issues in data mining and briefly introduce R-measure for attribution selection problem in DTI. We then present the DTI interactive visualisation system CABRO, based on R-measure and a combination of several DTI techniques, in which we focus on solutions to two problems: (1) support for understanding of large decision trees, and (2) support for interactive learning and model selection.

This abstract summarizes key points of our ongoing project that aims at developing several new methods in knowledge discovery in databases (KDD) and implementing them together with related techniques in an interactive-graphic environment. This research lies in several areas if KDD such as supervised classi fication, conceptual clustering, discovery of association rules, parallel and distributed data mining, genetic programming. Some of them are the continuation of our work done so far [1], [2], [3], and some others have been started recently