Performing the complex task of Knowledge Discovery in Databases (KDD) requires a break-down of the task-complexity to enable the possibilityofperforming the KDD-task. Since even more techniques will appear in the future that can solveavarietyof KDD-problems, a domain expert that wants to analyse his domain should have the means to work with tools that integrate several of these techniques as well as the techniques themselves. In this paper a framework is proposed for a strategy component that is to be used for a KDD-system that can guide users in breaking down the complexity of a typical KDD-task and supports him in selecting and using several ML-techniques. The goals

An important success factor for the field of KDD lies in the development and integration of methods for supporting the construction and execution of KDD processes. Crucial aspects

This paper describes research that is performed in the course of a project where a methodology for providing user support for KDD processes plays a central role. Although methodologically we aim at supporting the whole process of applying inductive learning techniques, the current paper focussus on a part of this process. The main issue in this paper is the support of data preprocessing for KDD. We give some insights in the metadata we calculate from a dataset as part of the method for user support. DCT (Data Characteristion Tool) is implemented in a software environment (Clementine). Some examples are given that resulted from running the UGM/DCT (User Guidance Module combined with DCT) on the data.

A growing interest in real-world applications of inductive techniques signifies the need for methodologies for applying them. So far a number of methodologies for applying inductive learning techniques are described. After reviewing several published approaches, a number of unsolved problems are discussed, two major problems being the lackofattention to nontechnical issues and the focus of most approaches on specific, well defined problems with a limited scope. We propose the MeDIA-model as a reference structure for the application of inductive learning techniques that covers the issues mentioned in other approaches and generalises from problem specific approaches. The model is part of a methodology that aims at supporting the application of inductive learning techniques in various settings, and helps to plan projects where suchtechniques are involved.

Dieser Artikel beschreibt die Forschung im Rahmen eines Projektes, bei dem die Bereitstellung einer Benutzerunterstützung innerhalb des KDD-Prozesses die zentrale Rolle spielt. Obwohl es prinzipiell das Ziel ist, den ganzen Prozess zu unterstützen, befaßt sich dieser Artikel lediglich mit einem Teil davon. Der Schwerpunkt liegt hierbei auf der Datenvorverarbeitungs-Phase, wobei ebenfalls ein erster Ansatz fur die Data Mining-Phase vorgestellt wird. Die Unterstützung basiert auf Datencharakteristiken (Meta-Daten), die einen vorliegenden Datensatz möglichst genau beschreiben und von dem innerhalb einer Softwareumgebung (Clementine) implementierten DCT (Data Characterisation Tool) berechnet werden. Wir stellen diese Maße vor und geben einige Beispiele fur die Benutzerunterstützung aufgrund konkreter Datensätze.

This paper describes research that is performed in the course of a project where a methodology for providing user support plays a central role. Although methodologically we aim at supporting the whole process of applying inductive learning techniques, the current paper focussus on support of the data preprocessing phase and getting insight in the data. One of our experiences is that preprocessing of data possibly is the most time consuming part of machine learning applications. We will rudimentary describe the metadata we calculate from a dataset as part of the method for user support and focus on how metadata can be used to guide preprocessing in combination with a top down approach. Some examples are given that resulted from running the UGM/DCT (User Guidance Module/Data Characterisation Tool) on example data. Finally we consider the improvements we made w.r.t. other approaches as well as what we gained using this extension to our User Guidance Module (UGM) for user support.

Knowledge Discovery in Databases (KDD) currently receives much attention from both the research as well as the industrial world. This might be due to the fact that companies are often faced with continuously growing databases that become increasingly important for decision making, whereas traditional data analysis approaches might not be able to handle all the requirements of decision makers. In such cases, KDD can offer an additional way for looking at data and extracting interesting knowledge in addition to more traditional approaches as e.g. statistics. Unfortunately, experiences with the application of Data Mining techniques show us that application of such techniques requires (thorough) expertise, and is often not so straightforward as hoped. Therefore, providing User Support for KDD processes is seen as necessary. The project described here is performed at the institute AIFB since early 1995 . An architecture for a User Guidance Module (UGM) is developed that aims at providing user support for KDD. The methodology that forms the basis for this architecture will be shortly described in this project report, together with some aspects of the prototype that is implemented in order to provide a proof of concept.