The Difference and the Norm — Characterising Similarities and Differences Between Databases

  • Kailash Budhathoki
  • Jilles VreekenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9285)


Suppose we are given a set of databases, such as sales records over different branches. How can we characterise the differences and the norm between these datasets? That is, what are the patterns that characterise the general distribution, and what are those that are important to describe the individual datasets? We study how to discover these pattern sets simultaneously and without redundancy – automatically identifying those patterns that aid describing the overall distribution, as well as those pointing out those that are characteristic for specific databases. We define the problem in terms of the Minimum Description Length principle, and propose the DiffNorm algorithm to approximate the MDL-optimal summary directly from data. Empirical evaluation on synthetic and real-world data shows that DiffNorm efficiently discovers descriptions that accurately characterise the difference and the norm in easily understandable terms.


Minimum Description Length Kolmogorov Complexity Frequent Pattern Mining Minimum Description Length Principle Sale Record 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Max Planck Institute for Informatics and Saarland UniversitySaarbrückenGermany

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