Abstract
In the past years pattern detection has gained in importance for many companies. As the volume of collected data increases so does typically the number of found patterns. To cope with this problem different interestingness measures for patterns have been proposed. Unfortunately, their usefulness turns out to be limited in practical applications. To address this problem, we propose a technique for a guided, visual exploration of patterns rather than presenting analysts with static ordered lists of patterns. Specifically, we focus on a method to guide drill-downs into hierarchical attributes, where we make use of change mining on frequent item sets for pattern discovery.
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Böttcher, M., Spott, M. (2013). Exploring Time Series of Patterns: Guided Drill-Down in Hierarchies Using Change Mining on Frequent Item Sets. In: Moewes, C., Nürnberger, A. (eds) Computational Intelligence in Intelligent Data Analysis. Studies in Computational Intelligence, vol 445. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32378-2_12
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DOI: https://doi.org/10.1007/978-3-642-32378-2_12
Publisher Name: Springer, Berlin, Heidelberg
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