Spatio-temporal data stream clustering method based on data field

Abstract

The invention discloses a spatial-temporal data stream clustering method based on a data field, and the method comprises the steps: firstly dividing a research region according to grids, distributingnew data to corresponding grid units according to the attribute values of the new data when the new data arrives, and adding the new data into a cache list of grids; attenuating the historical qualityof the grids every other calculation interval, calculating the new quality of the grids through a data field method, then updating the grid potential value and the data field parametersand finally dynamically adjusting a clustering result according to the change condition of the grid state. A data field theory is introduced into data stream clustering. The defect that the traditional data streamclustering algorithm is difficult to sense the correlation between the data is improved, and the method can perform effective dynamic clustering on the spatio-temporal data streams, so that the methodis applied to spatio-temporal data mining scenes such as urban hotspot dynamic detection and the like.

Description

technical field [0001] The invention belongs to the field of spatial data mining, and relates to a spatio-temporal data stream clustering method, in particular to a data field-based spatio-temporal data stream clustering algorithm. Background technique [0002] With the development of positioning and navigation equipment such as GPS and Beidou, more and more behavioral trajectory data are collected and saved. These data often record time and space information at the same time. With the help of increasingly powerful Internet of Things and communication technologies, they can pass The high-speed network continuously uploads to the cloud, forming a spatio-temporal data flow. Combining spatio-temporal data streams with incremental learning algorithms can discover the dynamic changes of spatial phenomena, which can be applied to dynamic spatial data mining scenarios such as real-time detection of urban hotspots. [0003] Clustering is an important spatial data mining method. Tra...

AI Technical Summary

Problems solved by technology

Spatial data is generated from geographic space, and its distribution conforms to the first law of geography, that is, there is a correlation between data that attenuates with the increase of time-space distance. Most of the current data stream clustering algorithms are transformed from the corresponding traditional clustering algorithms. Although it can discover the aggregation pattern of data, it is difficult to perceive the correlation between spatial data, so the traditional data flow clustering algorithm is difficult to effectively analyze and mine spatiotemporal data flow

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