Spatial clustering method based on time sequence correlation

A technology of spatial clustering and correlation, applied in the field of big data and data mining applications of spatial analysis, can solve the problem of not being able to obtain better clustering results, and achieve the effect of real results.

Inactive Publication Date: 2016-10-26
COMP NETWORK INFORMATION CENT CHINESE ACADEMY OF SCI
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Problems solved by technology

[0005] However, for objects with different spatial positions and temporal characteristics, traditional clustering methods have limitations and cannot obtain better clustering results

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  • Spatial clustering method based on time sequence correlation
  • Spatial clustering method based on time sequence correlation
  • Spatial clustering method based on time sequence correlation

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Embodiment Construction

[0018] In order to make the above objects, features and advantages of the present invention more obvious and understandable, the present invention will be further described below through specific embodiments and accompanying drawings.

[0019] The spatial clustering method based on temporal correlation of this embodiment, its step flow is as follows figure 1 As shown, it specifically includes the following steps:

[0020] The first step is to select a set of spatial points to be clustered. This collection includes all points within a certain spatial range, and for each point, time series data within a time period is included. For example, if the air quality monitoring stations in China are clustered, the set of spatial points includes all air quality monitoring stations, and each monitoring station includes hourly air quality inspection data.

[0021] The above-mentioned set of spatial points may be all spatial points within a spatial range, or may be filtered spatial points...

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Abstract

The invention relates to a spatial clustering method based on time sequence correlation. The method comprises the steps of: 1, selecting a set of spatial points to be clustered; 2, according to geographical relationships of the spatial points, carrying out first-time clustering, and clustering the spatial points belonging to the same geographical relationship into one category; 3, determining a time interval T of time sequence data, which is used in the process of carrying out second-time clustering, obtaining a data value of each spatial point in the time interval T, and forming a time sequence; 4, according to clustering results obtained in the step 2 and the time sequence obtained in the step 3, calculating time sequence correlation between any two spatial points in the same category; and 5, for each clustering result in the step 2, combining the time sequence correlation obtained in the step 4 to carry out second-time clustering on each clustering result so as to form a final clustering result. According to the spatial clustering method disclosed by the invention, two-step clustering is used in the spatial object clustering process, and consideration on the characteristics of time sequence correlation between the objects is added, so that the clustering result is more accurate and has greater practical significance.

Description

technical field [0001] The invention belongs to the application field of big data and data mining of spatial analysis, and in particular relates to a spatial clustering method based on temporal correlation. Background technique [0002] Clustering is an important component and analysis method in the field of data mining. With the wide application of big data and data mining, one of the commonly used methods in the field of data analysis - cluster analysis has also been more and more widely explored. It is widely used in image processing, biological information, spatial database, artificial intelligence, etc. It has been very effective in many fields. [0003] The main idea of ​​clustering is to classify data objects with high similarity into one cluster, while data objects between different clusters have no or low similarity, similar within clusters, and different between clusters. For cluster analysis, measuring the similarity between data objects has become the key to th...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
Inventor 杜一崔文娟吕菲周园春黎建辉
Owner COMP NETWORK INFORMATION CENT CHINESE ACADEMY OF SCI
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