Guest editors’ introduction: special issue of selected papers from ECML PKDD 2009

In this chapter we describe how to successfully apply the MDL principle to pattern mining. In particular, we discuss how pattern-based models can be designed and induced by means of compression, resulting in succinct and characteristic descriptions of the data.As motivation, we argue that traditional pattern mining asks the wrong question: instead of asking for all patterns satisfying some interestingness measure, one should ask for a small, non-redundant, and interesting set of patterns-which allows us to avoid the pattern explosion. Firmly rooted in algorithmic information theory, the approach we discuss in this chapter states that the best set of patterns is that set that compresses the data best. We formalize this problem using the Minimum Description Length (MDL) principle, describe useful model classes, and briefly discuss algorithmic approaches to inducing good models from data. Last but not least, we describe how the obtained models-in addition to showing the key patterns of the data-can be used for a wide range of data mining tasks; hence showing that MDL selects useful patterns.

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Mining and Using Sets of Patterns through Compression

Leeuwen

Vreeken

2014

Frequent Pattern Mining

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Interactive Data Exploration Using Pattern Mining

Leeuwen

2014

Lecture Notes in Computer Science

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Abstract. We live in the era of data and need tools to discover valuable information in large amounts of data. The goal of exploratory data mining is to provide as much insight in given data as possible. Within this field, pattern set mining aims at revealing structure in the form of sets of patterns. Although pattern set mining has shown to be an effective solution to the infamous pattern explosion, important challenges remain. One of the key challenges is to develop principled methods that allow user-and task-specific information to be taken into account, by directly involving the user in the discovery process. This way, the resulting patterns will be more relevant and interesting to the user. To achieve this, pattern mining algorithms will need to be combined with techniques from both visualisation and human-computer interaction. Another challenge is to establish techniques that perform well under constrained resources, as existing methods are usually computationally intensive. Consequently, they are only applied to relatively small datasets and on fast computers. The ultimate goal is to make pattern mining practically more useful, by enabling the user to interactively explore the data and identify interesting structure. In this paper we describe the state-of-the-art, discuss open problems, and outline promising future directions.

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Guest editors’ introduction: special issue of selected papers from ECML PKDD 2009

Cited by 2 publications

References 14 publications

Mining and Using Sets of Patterns through Compression

Mining and Using Sets of Patterns through Compression

Interactive Data Exploration Using Pattern Mining

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