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- Arno J. Knobbe
- 1999

An important aspect of data mining algorithms and systems is that they should scale well to large databases. A consequence of this is that most data mining tools are based on machine learningâ€¦ (More)

- Dennis Leman, A. J. Feelders, Arno J. Knobbe
- Data Mining and Knowledge Discovery
- 2008

Finding subsets of a dataset that somehow deviate from the norm, i.e. where something interesting is going on, is a classical Data Mining task. In traditional local pattern mining methods, suchâ€¦ (More)

- Arno J. Knobbe, Marc de Haas, Arno Siebes
- PKDD
- 2001

The fact that data is scattered over many tables causes many problems in the practice of data mining. To deal with this problem, one either constructs a single table by hand, or one uses aâ€¦ (More)

- Arno J. Knobbe, Eric K. Y. Ho
- PKDD
- 2006

Pattern discovery algorithms typically produce many interesting patterns. In most cases, patterns are reported based on their individual merits, and little attention is given to the interestingnessâ€¦ (More)

- Arno J. Knobbe, Eric K. Y. Ho
- KDD
- 2006

In this paper we present a new approach to mining binary data. We treat each binary feature (item) as a means of distinguishing two sets of examples. Our interest is in selecting from the total setâ€¦ (More)

In this paper we present LeGo, a generic framework that utilizes existing local pattern mining techniques for global modeling in a variety of diverse data mining tasks. In the spirit of well knownâ€¦ (More)

- Matthijs van Leeuwen, Arno J. Knobbe
- Data Mining and Knowledge Discovery
- 2012

Large data is challenging for most existing discovery algorithms, for several reasons. First of all, such data leads to enormous hypothesis spaces, making exhaustive search infeasible. Second, manyâ€¦ (More)

- Matthijs van Leeuwen, Arno J. Knobbe
- ECML/PKDD
- 2011

Large and complex data is challenging for most existing discovery algorithms, for several reasons. First of all, such data leads to enormous hypothesis spaces, making exhaustive search infeasible.â€¦ (More)

- Arno J. Knobbe, Arno Siebes, Bart Marseille
- PKDD
- 2002

The fact that data is scattered over many tables causes many problems in the practice of data mining. To deal with this problem, one either constructs a single table by propositionalisation, or usesâ€¦ (More)

- Wouter Duivesteijn, Arno J. Knobbe, A. J. Feelders, Matthijs van Leeuwen
- 2010 IEEE International Conference on Data Mining
- 2010

Whenever a dataset has multiple discrete target variables, we want our algorithms to consider not only the variables themselves, but also the interdependencies between them. We propose to use theseâ€¦ (More)