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Efficient space k kernel mining method for space data

A spatial data and spatial technology, applied in database indexing, structured data retrieval, electrical digital data processing, etc., can solve problems such as application and difficult models, and achieve the effect of reducing search space and strengthening robustness

Active Publication Date: 2021-03-05
ZHEJIANG GONGSHANG UNIVERSITY
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Problems solved by technology

However, the enumeration problem of the maximum space group is an NP-hard problem, and it is difficult to apply the model to some time-efficient applications

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  • Efficient space k kernel mining method for space data
  • Efficient space k kernel mining method for space data
  • Efficient space k kernel mining method for space data

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Embodiment Construction

[0024] In order to make the above-mentioned purpose, features and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0025] In the following description, a lot of specific details are set forth in order to fully understand the present invention, but the present invention can also be implemented in other ways different from those described here, and those skilled in the art can do it without departing from the meaning of the present invention. Therefore, the present invention is not limited by the specific implementation cases disclosed below.

[0026] A kind of high-efficiency space k-core mining method oriented to spatial data proposed by the application, the method includes a pruning strategy and an efficient boundary-based space k-core mining algorithm (bound-based algorithm for short), the specific implementation process of t...

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Abstract

The invention discloses an efficient space k kernel mining method for space data. In order to find a space k kernel sub-graph meeting distance and cohesiveness constraints at the same time, the invention provides a new sub-graph model, namely a space k kernel model, on a space data set, and the new sub-graph model meets three conditions: the distance between two vertexes is not greater than a distance threshold r; each vertex has at least k neighbors; and any hyper-graph of the method cannot meet the former two conditions. Considering the attributes of the quadtree and the k core, the invention provides a new pruning strategy, so that the search space is reduced more effectively. Meanwhile, an efficient bound-based algorithm is developed in combination with a new pruning strategy, so thata spatial k core can be quickly found in a large spatial data set. Therefore, the application of the efficient spatial k-kernel mining method oriented to spatial data has great benefits for mining ofpotential communities and identification of key modes of spatial data sets.

Description

technical field [0001] The invention belongs to the technical field of graph data mining, and in particular relates to an efficient spatial k-kernel mining method for spatial data. Background technique [0002] With the increase of GPS devices, more and more spatial data are generated every day. For example, in Twitter, users can send tweets with geographic location information. On Facebook, users can mark places visited by checking in and post information through geotagging. For the analysis of spatial data, an important issue is how to quickly identify the cohesive structure of the data. Various models have been proposed in current research, such as minimum covering circle and space clique model, which can capture the cohesive structure and groups in spatial data. However computing these models is usually time consuming. The spatial clique model using the clique concept requires that each pair of points in the mined subset be close in space, that is, the distance betwe...

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Application Information

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IPC IPC(8): G06F16/2458G06F16/22G06F16/29
CPCG06F16/2465G06F16/2246G06F16/29
Inventor 王潇杨刘玉峰陈晨聂坤孙仁杰张梦琪吴艳萍
Owner ZHEJIANG GONGSHANG UNIVERSITY
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