Hierarchical Hexagonal Clustering and Indexing

Space-filling curves (SFCs) represent an efficient and straightforward method for sparse-space indexing to transform an <i>n</i>-dimensional space into a one-dimensional representation. This is often applied for multidimensional point indexing which brings a better perspective for data a...

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Main Authors: Vojtěch Uher, Petr Gajdoš, Václav Snášel, Yu-Chi Lai, Michal Radecký
Format: Article
Language:English
Published: MDPI AG 2019-05-01
Series:Symmetry
Subjects:
Online Access:https://www.mdpi.com/2073-8994/11/6/731
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spelling doaj-d5d77db3ca74447c916fe3704688765b2020-11-25T01:34:41ZengMDPI AGSymmetry2073-89942019-05-0111673110.3390/sym11060731sym11060731Hierarchical Hexagonal Clustering and IndexingVojtěch Uher0Petr Gajdoš1Václav Snášel2Yu-Chi Lai3Michal Radecký4Department of Computer Science, VŠB-Technical University of Ostrava, Ostrava-Poruba 708 00, Czech RepublicDepartment of Computer Science, VŠB-Technical University of Ostrava, Ostrava-Poruba 708 00, Czech RepublicDepartment of Computer Science, VŠB-Technical University of Ostrava, Ostrava-Poruba 708 00, Czech RepublicDepartment of Computer Science and Information Engineering, National Taiwan University of Science and Technology, 43, Sec.4, Keelung Rd., Taipei 106, TaiwanDepartment of Computer Science, VŠB-Technical University of Ostrava, Ostrava-Poruba 708 00, Czech RepublicSpace-filling curves (SFCs) represent an efficient and straightforward method for sparse-space indexing to transform an <i>n</i>-dimensional space into a one-dimensional representation. This is often applied for multidimensional point indexing which brings a better perspective for data analysis, visualization and queries. SFCs are involved in many areas such as big data analysis and visualization, image decomposition, computer graphics and geographic information systems (GISs). The indexing methods subdivide the space into logic clusters of close points and they differ in various parameters including the cluster order, the distance metrics, and the pattern shape. Beside the simple and highly preferred triangular and square uniform grids, the hexagonal uniform grids have gained high interest especially in areas such as GISs, image processing and data visualization for the uniform distance between cells and high effectiveness of circle coverage. While the linearization of hexagons is an obvious approach for memory representation, it seems there is no hexagonal SFC indexing method generally used in practice. The main limitation of hexagons lies in lacking infinite decomposition into sub-hexagons and similarity of tiles on different levels of hierarchy. Our research aims at defining a fast and robust hexagonal SFC method. The Gosper fractal is utilized to preserve the benefits of hexagonal grids and to efficiently and hierarchically linearize points in a hexagonal grid while solving the non-convex shape and recursive transformation issues of the fractal. A comparison to other SFCs and grids is conducted to verify the robustness and effectiveness of our hexagonal method.https://www.mdpi.com/2073-8994/11/6/731space-filling curvepoint clusteringGosper curvehexagonal grid
collection DOAJ
language English
format Article
sources DOAJ
author Vojtěch Uher
Petr Gajdoš
Václav Snášel
Yu-Chi Lai
Michal Radecký
spellingShingle Vojtěch Uher
Petr Gajdoš
Václav Snášel
Yu-Chi Lai
Michal Radecký
Hierarchical Hexagonal Clustering and Indexing
Symmetry
space-filling curve
point clustering
Gosper curve
hexagonal grid
author_facet Vojtěch Uher
Petr Gajdoš
Václav Snášel
Yu-Chi Lai
Michal Radecký
author_sort Vojtěch Uher
title Hierarchical Hexagonal Clustering and Indexing
title_short Hierarchical Hexagonal Clustering and Indexing
title_full Hierarchical Hexagonal Clustering and Indexing
title_fullStr Hierarchical Hexagonal Clustering and Indexing
title_full_unstemmed Hierarchical Hexagonal Clustering and Indexing
title_sort hierarchical hexagonal clustering and indexing
publisher MDPI AG
series Symmetry
issn 2073-8994
publishDate 2019-05-01
description Space-filling curves (SFCs) represent an efficient and straightforward method for sparse-space indexing to transform an <i>n</i>-dimensional space into a one-dimensional representation. This is often applied for multidimensional point indexing which brings a better perspective for data analysis, visualization and queries. SFCs are involved in many areas such as big data analysis and visualization, image decomposition, computer graphics and geographic information systems (GISs). The indexing methods subdivide the space into logic clusters of close points and they differ in various parameters including the cluster order, the distance metrics, and the pattern shape. Beside the simple and highly preferred triangular and square uniform grids, the hexagonal uniform grids have gained high interest especially in areas such as GISs, image processing and data visualization for the uniform distance between cells and high effectiveness of circle coverage. While the linearization of hexagons is an obvious approach for memory representation, it seems there is no hexagonal SFC indexing method generally used in practice. The main limitation of hexagons lies in lacking infinite decomposition into sub-hexagons and similarity of tiles on different levels of hierarchy. Our research aims at defining a fast and robust hexagonal SFC method. The Gosper fractal is utilized to preserve the benefits of hexagonal grids and to efficiently and hierarchically linearize points in a hexagonal grid while solving the non-convex shape and recursive transformation issues of the fractal. A comparison to other SFCs and grids is conducted to verify the robustness and effectiveness of our hexagonal method.
topic space-filling curve
point clustering
Gosper curve
hexagonal grid
url https://www.mdpi.com/2073-8994/11/6/731
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AT petrgajdos hierarchicalhexagonalclusteringandindexing
AT vaclavsnasel hierarchicalhexagonalclusteringandindexing
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