1,084 research outputs found
Essential Constraints of Edge-Constrained Proximity Graphs
Given a plane forest of points, we find the minimum
set of edges such that the edge-constrained minimum spanning
tree over the set of vertices and the set of constraints contains .
We present an -time algorithm that solves this problem. We
generalize this to other proximity graphs in the constraint setting, such as
the relative neighbourhood graph, Gabriel graph, -skeleton and Delaunay
triangulation. We present an algorithm that identifies the minimum set
of edges of a given plane graph such that for , where is the
constraint -skeleton over the set of vertices and the set of
constraints. The running time of our algorithm is , provided that the
constrained Delaunay triangulation of is given.Comment: 24 pages, 22 figures. A preliminary version of this paper appeared in
the Proceedings of 27th International Workshop, IWOCA 2016, Helsinki,
Finland. It was published by Springer in the Lecture Notes in Computer
Science (LNCS) serie
Higher signature Delaunay decompositions
A Delaunay decomposition is a cell decomposition in R^d for which each cell
is inscribed in a Euclidean ball which is empty of all other vertices. This
article introduces a generalization of the Delaunay decomposition in which the
Euclidean balls in the empty ball condition are replaced by other families of
regions bounded by certain quadratic hypersurfaces. This generalized notion is
adaptable to geometric contexts in which the natural space from which the point
set is sampled is not Euclidean, but rather some other flat semi-Riemannian
geometry, possibly with degenerate directions. We prove the existence and
uniqueness of the decomposition and discuss some of its basic properties. In
the case of dimension d = 2, we study the extent to which some of the
well-known optimality properties of the Euclidean Delaunay triangulation
generalize to the higher signature setting. In particular, we describe a higher
signature generalization of a well-known description of Delaunay decompositions
in terms of the intersection angles between the circumscribed circles.Comment: 25 pages, 6 figure
Sparse geometric graphs with small dilation
Given a set S of n points in R^D, and an integer k such that 0 <= k < n, we
show that a geometric graph with vertex set S, at most n - 1 + k edges, maximum
degree five, and dilation O(n / (k+1)) can be computed in time O(n log n). For
any k, we also construct planar n-point sets for which any geometric graph with
n-1+k edges has dilation Omega(n/(k+1)); a slightly weaker statement holds if
the points of S are required to be in convex position
Dense point sets have sparse Delaunay triangulations
The spread of a finite set of points is the ratio between the longest and
shortest pairwise distances. We prove that the Delaunay triangulation of any
set of n points in R^3 with spread D has complexity O(D^3). This bound is tight
in the worst case for all D = O(sqrt{n}). In particular, the Delaunay
triangulation of any dense point set has linear complexity. We also generalize
this upper bound to regular triangulations of k-ply systems of balls, unions of
several dense point sets, and uniform samples of smooth surfaces. On the other
hand, for any n and D=O(n), we construct a regular triangulation of complexity
Omega(nD) whose n vertices have spread D.Comment: 31 pages, 11 figures. Full version of SODA 2002 paper. Also available
at http://www.cs.uiuc.edu/~jeffe/pubs/screw.htm
A Framework for Algorithm Stability
We say that an algorithm is stable if small changes in the input result in
small changes in the output. This kind of algorithm stability is particularly
relevant when analyzing and visualizing time-varying data. Stability in general
plays an important role in a wide variety of areas, such as numerical analysis,
machine learning, and topology, but is poorly understood in the context of
(combinatorial) algorithms. In this paper we present a framework for analyzing
the stability of algorithms. We focus in particular on the tradeoff between the
stability of an algorithm and the quality of the solution it computes. Our
framework allows for three types of stability analysis with increasing degrees
of complexity: event stability, topological stability, and Lipschitz stability.
We demonstrate the use of our stability framework by applying it to kinetic
Euclidean minimum spanning trees
Efficient Computation of Multiple Density-Based Clustering Hierarchies
HDBSCAN*, a state-of-the-art density-based hierarchical clustering method,
produces a hierarchical organization of clusters in a dataset w.r.t. a
parameter mpts. While the performance of HDBSCAN* is robust w.r.t. mpts in the
sense that a small change in mpts typically leads to only a small or no change
in the clustering structure, choosing a "good" mpts value can be challenging:
depending on the data distribution, a high or low value for mpts may be more
appropriate, and certain data clusters may reveal themselves at different
values of mpts. To explore results for a range of mpts values, however, one has
to run HDBSCAN* for each value in the range independently, which is
computationally inefficient. In this paper, we propose an efficient approach to
compute all HDBSCAN* hierarchies for a range of mpts values by replacing the
graph used by HDBSCAN* with a much smaller graph that is guaranteed to contain
the required information. An extensive experimental evaluation shows that with
our approach one can obtain over one hundred hierarchies for the computational
cost equivalent to running HDBSCAN* about 2 times.Comment: A short version of this paper appears at IEEE ICDM 2017. Corrected
typos. Revised abstrac
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