Clusters of high-dimensional interval data and related Boolean functions of events in Euclidean space


Clustering interval data has been studied for decades. High-dimensional interval data can be expressed in terms of hyperrectangles in \(\mathbb {R}^d\) (or d-orthotopes) in case of real-valued d-attributes data. This paper investigates such high-dimensional interval data: the Cartesian product of intervals, or a vector of interval. For the efficient computation of related Boolean functions, some interesting aspects have been discovered using vertices and edges of the graph, generated from given events. We also study the lower and upper-bounded orthants in \(\mathbb {R}^d\) as events for which we show the existence of a polynomial-time algorithm to calculate the probability of the union of such events. This efficient algorithm has been discovered by constructing a suitable partial order relation based on a recursive projection onto lower-dimensional spaces. Illustrative real-life applications are presented.

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It is an honor for the first author to have his academic father, Professor András Prékopa (1929–2016) as a second author of this paper. This paper’s main topic: the probability of Boolean functions of high dimensional interval data, was studied in 2019 - 2020 solely by the first author, and he presented the main idea of this paper at ISAIM (International Symposium of Artificial Intelligence and Mathematics) in January 2020 in Fort Lauderdale, Florida. Working on Boolean functions of hyperrectangles and related binomial moment problem formulation was initially suggested by Professor Prékopa in May 2016. The first author dearly misses him.

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Correspondence to Jinwook Lee.

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András Prékopa: Deceased 18 September 2016.

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Lee, J., Prékopa, A. Clusters of high-dimensional interval data and related Boolean functions of events in Euclidean space. Ann Oper Res (2021).

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  • Clustering
  • Multivariate interval data
  • Orthant
  • Hyperrectangle
  • Graph
  • Spanning tree
  • Boolean functions
  • Euclidean space
  • Probability bounds