Group discovery algorithm model based on big data mining and analysis module

Abstract

The invention provides a group discovery algorithm model based on big data mining, and belongs to the technical field of big data mining. The method comprises the following steps: acquiring each pieceof trajectory data of a target, and preprocessing the trajectory data; taking each piece of effective trajectory data of the target as a midpoint, and intercepting the preprocessed data for a specified time length; recording the occurrence frequency of other people in the cut slice; preliminarily determining persons in the same row by utilizing the slices; acquiring trajectory data of a target and persons in the same row and preprocessing the trajectory data; taking each effective footprint as a starting point, and intercepting the time sequence trajectory data for a known fixed length; and calculating the occurrence frequency of all targets and persons in the same row. According to the method, people who may participate in group activities together with the targets are searched through attributes such as time, coordinates and names of people entering an area uploaded by each station and the known targets.

Description

technical field [0001] The invention relates to the field of big data mining, more specifically, a group discovery algorithm model based on big data mining. Background technique [0002] Community discovery has a long history of research and takes different forms in different disciplines. It is closely related to the ideas of graph segmentation in graph theory and computer science and hierarchical clustering in social networks. [0003] Graph segmentation is an important research problem in the field of parallel computing. Suppose there are n computing processors that can communicate (the processing area is not intended to communicate with all other processors). Based on this, a network can be built in which nodes represent processors, and edges between nodes link two nodes communicating with each other. The problem to be solved in parallel computing is to assign the same number of tasks to each node and minimize the communication between nodes, that is, to make the numbe...

AI Technical Summary

Problems solved by technology

For example, under the premise of knowing the number of communities, Wu and Huberman proposed a fast segmentation algorithm based on the voltage spectrum of the resistor network. , to find the entire community where a node is located, which is impossible for many algorithms

Images