### Abstract

We study two fundamental problems dealing with curves in the plane, namely, the nearest-neighbor problem and the center problem. Let C be a set of n polygonal curves, each of size m. In the nearest-neighbor problem, the goal is to construct a compact data structure over C, such that, given a query curve Q, one can efficiently find the curve in C closest to Q. In the center problem, the goal is to find a curve Q, such that the maximum distance between Q and the curves in C is minimized. We use the well-known discrete Fréchet distance function, both under L_{∞} and under L_{2}, to measure the distance between two curves. For the nearest-neighbor problem, despite discouraging previous results, we identify two important cases for which it is possible to obtain practical bounds, even when m and n are large. In these cases, either Q is a line segment or C consists of line segments, and the bounds on the size of the data structure and query time are nearly linear in the size of the input and query curve, respectively. The returned answer is either exact under L_{∞}, or approximated to within a factor of 1 + ε under L_{2}. We also consider the variants in which the location of the input curves is only fixed up to translation, and obtain similar bounds, under L_{∞}. As for the center problem, we study the case where the center is a line segment, i.e., we seek the line segment that represents the given set as well as possible. We present near-linear time exact algorithms under L_{∞}, even when the location of the input curves is only fixed up to translation. Under L_{2}, we present a roughly O(n^{2}m^{3}) -time exact algorithm.

Original language | English (US) |
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Title of host publication | Algorithms and Data Structures - 16th International Symposium, WADS 2019, Proceedings |

Editors | Zachary Friggstad, Mohammad R. Salavatipour, Jörg-Rüdiger Sack |

Publisher | Springer-Verlag |

Pages | 28-42 |

Number of pages | 15 |

ISBN (Print) | 9783030247652 |

DOIs | |

State | Published - Jan 1 2019 |

Event | 16th International Symposium on Algorithms and Data Structures, WADS 2019 - Edmonton, Canada Duration: Aug 5 2019 → Aug 7 2019 |

### Publication series

Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 11646 LNCS |

ISSN (Print) | 0302-9743 |

ISSN (Electronic) | 1611-3349 |

### Conference

Conference | 16th International Symposium on Algorithms and Data Structures, WADS 2019 |
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Country | Canada |

City | Edmonton |

Period | 8/5/19 → 8/7/19 |

### Fingerprint

### Keywords

- (Approximation) algorithms
- Clustering
- Data structures
- Fréchet distance
- Nearest-neighbor queries
- Polygonal curves

### ASJC Scopus subject areas

- Theoretical Computer Science
- Computer Science(all)

### Cite this

*Algorithms and Data Structures - 16th International Symposium, WADS 2019, Proceedings*(pp. 28-42). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11646 LNCS). Springer-Verlag. https://doi.org/10.1007/978-3-030-24766-9_3

**Efficient nearest-neighbor query and clustering of planar curves.** / Aronov, Boris; Filtser, Omrit; Horton, Michael; Katz, Matthew J.; Sheikhan, Khadijeh.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Algorithms and Data Structures - 16th International Symposium, WADS 2019, Proceedings.*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11646 LNCS, Springer-Verlag, pp. 28-42, 16th International Symposium on Algorithms and Data Structures, WADS 2019, Edmonton, Canada, 8/5/19. https://doi.org/10.1007/978-3-030-24766-9_3

}

TY - GEN

T1 - Efficient nearest-neighbor query and clustering of planar curves

AU - Aronov, Boris

AU - Filtser, Omrit

AU - Horton, Michael

AU - Katz, Matthew J.

AU - Sheikhan, Khadijeh

PY - 2019/1/1

Y1 - 2019/1/1

N2 - We study two fundamental problems dealing with curves in the plane, namely, the nearest-neighbor problem and the center problem. Let C be a set of n polygonal curves, each of size m. In the nearest-neighbor problem, the goal is to construct a compact data structure over C, such that, given a query curve Q, one can efficiently find the curve in C closest to Q. In the center problem, the goal is to find a curve Q, such that the maximum distance between Q and the curves in C is minimized. We use the well-known discrete Fréchet distance function, both under L∞ and under L2, to measure the distance between two curves. For the nearest-neighbor problem, despite discouraging previous results, we identify two important cases for which it is possible to obtain practical bounds, even when m and n are large. In these cases, either Q is a line segment or C consists of line segments, and the bounds on the size of the data structure and query time are nearly linear in the size of the input and query curve, respectively. The returned answer is either exact under L∞, or approximated to within a factor of 1 + ε under L2. We also consider the variants in which the location of the input curves is only fixed up to translation, and obtain similar bounds, under L∞. As for the center problem, we study the case where the center is a line segment, i.e., we seek the line segment that represents the given set as well as possible. We present near-linear time exact algorithms under L∞, even when the location of the input curves is only fixed up to translation. Under L2, we present a roughly O(n2m3) -time exact algorithm.

AB - We study two fundamental problems dealing with curves in the plane, namely, the nearest-neighbor problem and the center problem. Let C be a set of n polygonal curves, each of size m. In the nearest-neighbor problem, the goal is to construct a compact data structure over C, such that, given a query curve Q, one can efficiently find the curve in C closest to Q. In the center problem, the goal is to find a curve Q, such that the maximum distance between Q and the curves in C is minimized. We use the well-known discrete Fréchet distance function, both under L∞ and under L2, to measure the distance between two curves. For the nearest-neighbor problem, despite discouraging previous results, we identify two important cases for which it is possible to obtain practical bounds, even when m and n are large. In these cases, either Q is a line segment or C consists of line segments, and the bounds on the size of the data structure and query time are nearly linear in the size of the input and query curve, respectively. The returned answer is either exact under L∞, or approximated to within a factor of 1 + ε under L2. We also consider the variants in which the location of the input curves is only fixed up to translation, and obtain similar bounds, under L∞. As for the center problem, we study the case where the center is a line segment, i.e., we seek the line segment that represents the given set as well as possible. We present near-linear time exact algorithms under L∞, even when the location of the input curves is only fixed up to translation. Under L2, we present a roughly O(n2m3) -time exact algorithm.

KW - (Approximation) algorithms

KW - Clustering

KW - Data structures

KW - Fréchet distance

KW - Nearest-neighbor queries

KW - Polygonal curves

UR - http://www.scopus.com/inward/record.url?scp=85070596288&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85070596288&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-24766-9_3

DO - 10.1007/978-3-030-24766-9_3

M3 - Conference contribution

AN - SCOPUS:85070596288

SN - 9783030247652

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 28

EP - 42

BT - Algorithms and Data Structures - 16th International Symposium, WADS 2019, Proceedings

A2 - Friggstad, Zachary

A2 - Salavatipour, Mohammad R.

A2 - Sack, Jörg-Rüdiger

PB - Springer-Verlag

ER -