Abstract
The aim of redescription mining is to find valid redescriptions for given data, query language, similarity relation, and user-specified constraints. In other words, we need to explore the search space consisting of query pairs from the query language, looking for those pairs that have similar enough support in the data and that satisfy the other constraints. In this chapter, we present the different methods that have been proposed to carry out this exploration efficiently. Existing methods can be arranged into three main categories: (1) mine-and-pair approaches, (2) alternating approaches, and (3) approaches that use atomic updates. We consider each one in turn, explaining its general common principles and looking at different algorithms designed on these principles. Next, we compare the different methods and discuss their relative strengths and weaknesses. Finally, we consider how to adapt the algorithms to handle cases where some values are missing from the input data.
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Notes
- 1.
The equation for the pessimistic Jaccard index presented by Galbrun and Miettinen (2012, Equation 5.7) is erroneous, as it misses two summands from the denominator.
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Galbrun, E., Miettinen, P. (2017). Algorithms for Redescription Mining. In: Redescription Mining. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-72889-6_2
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