From sets of good redescriptions to good sets of redescriptions
Résumé
Redescription mining aims at finding pairs of queries over data variables that
describe roughly the same set of observations. These redescriptions can be used to obtain
different views on the same set of entities. So far, redescriptionminingmethods have aimed at
listing all redescriptions supported by the data. Such an approach can result in many redundant
redescriptions and hinder the user’s ability to understand the overall characteristics of the data.
In this work, we present an approach to identify and remove the redundant redescriptions,
that is, an approach to move from a set of good redescriptions to a good set of redescriptions.
We measure the redundancy of a redescription using a framework inspired by the concept of
subjective interestingness based on maximum entropy distributions as proposed by De Bie
(Data Min Knowl Discov 23(3):407–446, 2011). Redescriptions, however, generate specific
requirements on the framework, and our solution differs significantly from the existing ones.
Notably, our approach can handle disjunctions and conjunctions in the queries, whereas the
existing approaches are limited only to conjunctive queries. Our framework can also handle
data with Boolean, nominal, or real-valued data, possibly containing missing values, making
it applicable to a wide variety of data sets. Our experiments show that our framework can
efficiently reduce the redundancy even on large data sets.
Origine | Fichiers produits par l'(les) auteur(s) |
---|
Loading...