We present a new approach to mining redescriptions -- patterns that identify subsets of data that afford multiple definitions. Redescription mining finds important applications in descriptor-rich datasets, such as in bioinformatics. The key contributions of this paper are (i) identifying the existence of a dichotomy law that the redescriptions follow (ii) definition of a notion of irredundant redescriptions underyling a dataset, (iii) an output-sensitive algorithm to mine the irredundant set of redescriptions in a specific form, both exact and approximate, (iv) identifying an important connection between biclusters and redescriptions, using which we can build a redescription mining algorithm around a biclustering algorithm.
By: Laxmi Parida; Naren Ramakrishna
Published in: RC23565 in 2005
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