Learning What Matters – Sampling Interesting Patterns

Conference paper

DOI: 10.1007/978-3-319-57454-7_42

Part of the Lecture Notes in Computer Science book series (LNCS, volume 10234)
Cite this paper as:
Dzyuba V., van Leeuwen M. (2017) Learning What Matters – Sampling Interesting Patterns. In: Kim J., Shim K., Cao L., Lee JG., Lin X., Moon YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science, vol 10234. Springer, Cham

Abstract

In the field of exploratory data mining, local structure in data can be described by patterns and discovered by mining algorithms. Although many solutions have been proposed to address the redundancy problems in pattern mining, most of them either provide succinct pattern sets or take the interests of the user into account—but not both. Consequently, the analyst has to invest substantial effort in identifying those patterns that are relevant to her specific interests and goals.

To address this problem, we propose a novel approach that combines pattern sampling with interactive data mining. In particular, we introduce the LetSIP algorithm, which builds upon recent advances in (1) weighted sampling in SAT and (2) learning to rank in interactive pattern mining. Specifically, it exploits user feedback to directly learn the parameters of the sampling distribution that represents the user’s interests.

We compare the performance of the proposed algorithm to the state-of-the-art in interactive pattern mining by emulating the interests of a user. The resulting system allows efficient and interleaved learning and sampling, thus user-specific anytime data exploration. Finally, LetSIP demonstrates favourable trade-offs concerning both quality–diversity and exploitation–exploration when compared to existing methods.

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceKU LeuvenLeuvenBelgium
  2. 2.LIACSLeiden UniversityLeidenThe Netherlands

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