Patent
Grid-based spatial multi-scale fast clustering method
Gui Zhipeng,Long Xi,Dehua Peng,Wu Huayi +3 more
- 14 Sep 2018
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TL;DR: In this article, a grid-based spatial multi-scale fast clustering method is proposed, which includes the following steps that: S1, a data scale is selected, the size of grids is determined, gridding is performed on sample data, and the density values of the grids are put into statistics; S2, an initial density threshold is specified, all grids satisfying the threshold condition are reserved, and apreliminary density matrix is obtained; S3, a filter template is specified according to an observation scale, and convolution operation is performed, a connected region is generated
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Abstract: The invention discloses a grid-based spatial multi-scale fast clustering method. The method includes the following steps that: S1, a data scale is selected, the size of grids is determined, gridding is performed on sample data, and the density values of the grids is put into statistics; S2, an initial density threshold is specified, all grids satisfying the threshold condition are reserved, and apreliminary density matrix is obtained; S3, a filter template is specified according to an observation scale, and convolution operation is performed on a global grid space; S4, a connected region is generated through neighborhood search so as to be adopted as a preliminary clustering result, integration operation is performed on the grids, the grid space is mapped onto an original point set, and an original point set clustering result is obtained; S5, the observation scale is adjusted, a transformed new filter is adopted to perform operation in the S3 and S4 on a result matrix again, a clustering result of the next observation scale is obtained; and S6, the data scale is changed, the S1 to S5 are repeated, clustering results under different data scales are obtained. The algorithm of the invention has the advantages of low complexity, high clustering efficiency and high precision, and can meet the requirements of real-time multi-scale clustering and visual analysis of massive point sets.
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Wang Fei,Wang Guoyin,Yang Jie,Li Yuan,Ouyang Weihua +4 more
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TL;DR: In this article, a grid clustering algorithm based on a density peak value (DPEV) is proposed to protect a grid algorithm and can efficiently process large-scale data, and the algorithm comprises the following steps that: firstly, pelletizing an N-dimensional space into nonintersecting rectangular grid unit, and then, carrying out statistics on the information of unit space; utilizing a thought that a DPEV clustering is used for searching a center point to determine a center unit, i.e., surrounding the center grid unit by certain data units with
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Patent
Mass spatial data density clustering method based on elastic distribution dataset
Shen Ye,Zhou Tianhe,Li Sijian,Ren Peirong +3 more
- 11 Aug 2017
TL;DR: In this paper, a mass spatial data density clustering method based on an elastic distribution dataset is proposed, where automatic meshing and data distribution are performed according to distribution of data in space based on the design ideology of "RDD partition--intra-partition parallel computing--local result merging" targeting quick mining of an aggregation characteristic base of large-scale spatial data, so that data volumes in meshes are relatively balanced, and the purpose of balancing arithmetic node loads is achieved.
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Patent
Hierarchical grid partition method under multi-scale precision control
Wang Guoping,Zhang Huijuan +1 more
- 18 Feb 2015
TL;DR: In this paper, a hierarchical grid partition method under multi-scale precision control is proposed, which includes calculating shape information corresponding to each partition result; using the shape information calculated in the last time for guidance during iteration partition in the next time; taking local geometrical information of a model into consideration during partition to complementally deal with a condition that a partitioned area is too complex to describe with a simple shape.
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Patent
Data clustering apparatus and method
Bum Joon Seo,Hyung Chan Kim,Kyu Sam Oh,Soonhwan Kwon,Min Hwan Oh +4 more
- 16 Jun 2014
TL;DR: In this article, the data clustering apparatus includes an index discriminating unit discriminating an index corresponding to an input position of new data input to a space for data-clustering, including a lattice-type segmented space having lattice unit spaces set with different indexes, and a clustering unit creating a new cluster in the discriminated index using the input new data as a representative value when a cluster is not created at a discriminated index.
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