Rüdiger Wirth

A New Algorithm for Faster Mining of Generalized Association Rules

Generalized association rules are a very important extension of boolean association rules, but with current approaches mining generalized rules is computationally very expensive. Especially when considering the rule generation as being part of an interactive KDD-process this becomes annoying. In this paper we discuss strengths and weaknesses of known approaches to generate frequent itemsets. Based on the insights we derive a new algorithm, called Prutax, to mine generalized frequent itemsets. The basic ideas of the algorithm and further optimisation are described. Experiments with both synthetic and real-life data show that Prutax is an order of magnitude faster than previous approaches.

Knowledge-based support of system analysis for the analysis of Failure modes and effects☆

Abstract Failure mode and effects analysis (FMEA) is an important method of preventive quality assurance. However, even 30 years after its introduction in the aerospace industry and despite more than 10 years of experience in using this method in development, FMEA is still a challenge for many companies. This paper analyses the problems with the conventional way of carrying out an FMEA. It is argued that a knowledge-based approach to FMEA can alleviate most of these problems. The solution chosen in the WIFA project is presented. WIFA employs various knowledge bases to support complete and precise descriptions of processes and products, and to facilitate the later reuse of the knowledge collected during an FMEA. The essential features of these knowledge bases, and their use in FMEA, are described.

Predictive modeling in automotive direct marketing: tools, experiences and open issues

Direct marketing is an increasingly popular application of data mining. In this paper we summarize some of our own experiences from various data mining application projects for direct marketing. We focus on a particular project environment and describe tools which address issues across the whole data mining process. These tools include a Quick Reference Guide for the standardization of the process and for user guidance and a library of re-usable procedures in the commercial data mining tool Clementine. We report experiences with these tools and identify open issues requiring further research. In particular, we focus on evaluation measures for predictive models.

Towards Process-Oriented Tool Support for Knowledge Discovery in Databases

Knowledge Discovery in Databases (KDD) is currently a hot topic in industry and academia. Although KDD is now widely accepted as a complex process of many different phases, the focus of research behind most emerging products is on underlying algorithms and modelling techniques. The main bottleneck for KDD applications is not the lack of techniques. The challenge is to exploit and combine existing algorithms effectively, and help the user during all phases of the KDD process. In this paper, we describe the project Citrus which addresses these practically relevant issues. Starting from a commercially available system, we develop a scaleable, extensible tool inherently based on the view of KDD as an interactive and iterative process. We sketch the main components of this system, namely an information manager for effective retrieval of data and results, an execution server for efficient execution, and a process support interface for guiding the user through the process.

Wissensentdeckung in Datenbanken und Data Mining: Ein überblick

Dieser Artikel gibt einen uberblick uber das Gebiet der Wissensentdeckung in Datenbanken und Data Mining. Ferner gibt der Artikel eine ubersicht zu existierenden Techniken, Werkzeugen und Anwendungen in wissenschaftlicher Forschung und industrieller Praxis. Die verschiedenen Phasen des Prozesses der Wissensentdeckung werden vorgestellt und analysiert. Es gibt eine Reihe von Data Mining Zielen, die sich durch Anwendung des extrahierten Wissens bearbeiteten lassen. Wir beschreiben diese Ziele und stellen die entsprechenden Verfahren vor, die zur Erreichung dieser Ziele geeignet sind. Solche Verfahren basieren auf statistischen Methoden, neuronalen Netzen, Case-Based Reasoning und symbolischen Lernverfahren. Einige wichtige Phasen des Prozesses, wie die Vorbereitung der Daten, die eigentliche Entdeckung neuen Wissens und Bewertung der Ergebnisse werden wir ausfuhrlicher diskutieren. Inzwischen hat die Wissensentdeckung in Datenbanken in verschiedenen Gebieten zahlreiche Anwendungen gefunden. Auserdem sind die Anzahl der existierenden Systeme fur die Wissensentdeckung explosionsartig in die Hohe gestiegen. Aus diesem Grund ist eine Beschreibung diverser Anwendungen und die Vorstellung aller existierender Systeme nicht moglich. Wir stellen jedoch einige Anwendungen vor und beschreiben einige ausgewahlte Systeme. Ein uberblick uber die aktuellen Forschungsthemen schliest den Artikel ab.

Discovery of Association Rules over Ordinal Data: A New and Faster Algorithm and Its Application to Basket Analysis

This paper argues that quantitative information like prices, amounts bought, and time can give valuable insights into consumer behavior. While Boolean association rules discard any quantitative information, existing algorithms for quantitative association rules can hardly be used for basket analysis. They either lack performance, are restricted to the two-dimensional case, or make questionable assumptions about the data. We propose a new and faster algorithm Q2 for the discovery of multi-dimensional association rules over ordinal data, which is based on ideas presented in [SA96]. Our new algorithm Q2 does not search for quantitative association rules from the very beginning. Instead Q2 prunes out a lot of candidates by first computing the frequent Boolean itemsets. After that, the frequent quantitative itemsets are found in a single pass over the data.

Exploration of Ordinal Data Using Association Rules

The discovery of association rules is a very efficient data mining technique that is especially suitable for large amounts of categorical data. This paper shows how the discovery of association rules can be of benefit for numeric data as well. Based on a review of previous approaches we introduce Q2, a faster algorithm for the discovery of multi-dimensional association rules over ordinal data. We experimentally compare the new algorithm with the previous approach, obtaining performance improvements of more than an order of magnitude on supermarket data. In addition, a new absolute measure for the interestingness of quantitative association rules is introduced. It is based on the view that quantitative association rules have to be interpreted with respect to their Boolean generalizations. This measure has two major benefits compared to the previously used relative interestingness measure; first, it speeds up rule extraction and evaluation and second, it is easier to interpret for a user. Finally we introduce a rule browser which supports the exploration of ordinal data with quantitative association rules.