Can I read Computing Optimal K-regret Minimizing Sets with Top-k Depth Contours on EtoBox?

Computing Optimal K-regret Minimizing Sets with Top-k Depth Contours by Chester, Sean; Thomo, Alex; Venkatesh, S.; Whitesides, Sue is a scholarly article available to read on EtoBox.

What is Computing Optimal K-regret Minimizing Sets with Top-k Depth Contours about?

Regret minimizing sets are a very recent approach to representing a dataset D with a small subset S of representative tuples. The set S is chosen such that executing any top-1 query on S rather than D is minimally perceptible to any user. To discover an optimal regret minimizing set of a predetermined cardinality is conjectured to be a hard problem. In this paper, we generalize the problem to that of finding an optimal k$regret minimizing set, wherein the difference is computed over top-k queries, rather than top-1 queries. We adapt known geometric ideas of top-k depth contours and the reverse top-k problem. We show that the depth contours themselves offer a means of comparing the optimality of regret minimizing sets using L2 distance. We design an O(cn^2) plane sweep algorithm for two dimensions to compute an optimal regret minimizing set of cardinality c. For higher dimensions, we introduce a greedy algorithm that progresses towards increasingly optimal solutions by exploiting the transitivity of L2 distance.

Author

Chester, Sean; Thomo, Alex; Venkatesh, S.; Whitesides, Sue

Published

2012

Language

EN