Interactive data exploration using pattern mining

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.