Knowledge-based multi-level co-location pattern mining method

A multi-level, bit-pattern technology, applied in other database retrieval, structured data retrieval, instruments, etc., can solve a large number of connection operations, difficult to find and other problems, to avoid connection operations and alleviate limitations.

Pending Publication Date: 2022-01-28
GUILIN UNIV OF ELECTRONIC TECH
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Problems solved by technology

[0004] The purpose of the present invention is to provide a knowledge-based multi-level homolocation pattern mining method, aiming to solve the technical problem that the homolocation pattern discovery method in the prior art requires a large number of connection operations and is difficult to find a very large group when generating table instances, And alleviating the limitation that existing homolocation pattern mining methods can only discover single-level homolocation patterns of fine-grained features

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  • Knowledge-based multi-level co-location pattern mining method
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  • Knowledge-based multi-level co-location pattern mining method

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[0032] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0033] see figure 1 , the present invention proposes a knowledge-based multi-level homolocation pattern mining method, comprising the following steps:

[0034] S1: Combining the domain knowledge and spatial data represented by ontology to obtain the semantic relationship of the spatial data;

[0035] S2: Obtain the star-shaped neighbor relationship of the spatial dataset according to the distance threshold;

[0036] S3: Obtain the cluster instance according to the star neighbor;

[0037] S4: Obtain the hash structure according...

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Abstract

The invention discloses a knowledge-based multi-level co-location pattern mining method. The method comprises the following steps: firstly, obtaining a semantic relationship of spatial data by combining domain knowledge represented by an ontology and the spatial data; according to a distance threshold value, obtaining a star neighborhood relation of the spatial data set; and obtaining a group instance according to the star neighbor. By means of the method for discovering the co-location mode through the cluster instances, a large amount of connection operation for generating the table instances is avoided, and the difficulty in discovering the large clusters is relieved. The technical problems that in the prior art, a co-location pattern discovery method needs a large number of connection operations when table instances are generated, and a great cluster is difficult to discover are solved. And meanwhile, the limitation that the existing co-location mode mining method can only discover the single-level co-location mode of the fine-grained features is relieved.

Description

technical field [0001] The invention relates to the technical field of data mining, in particular to a knowledge-based multi-level homolocation pattern mining method. Background technique [0002] Spatial colocation pattern mining is an important branch of spatial association analysis. Colocation patterns obtained through spatial colocation pattern mining methods can serve various fields, including ecological and environmental management, urban services, and business services. Spatial colocation pattern mining aims to discover the set of occurrence features where instances are often adjacent in space. [0003] As a branch of data mining, spatial data mining is closely related to transactional data mining. The introduction of Apriori algorithm is a milestone in the history of data mining. Afterwards, many interesting methods similar to Apriori for discovering isotopic patterns have been proposed one after another. These methods generate high-order homogeneous patterns thro...

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Application Information

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IPC IPC(8): G06F16/2458G06F16/29G06F16/901
CPCG06F16/2465G06F16/29G06F16/9024
Inventor 包旭光王龙常亮古天龙
Owner GUILIN UNIV OF ELECTRONIC TECH
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