Graph-data-oriented projection clustering method

Abstract

The invention discloses a graph-data-oriented projection clustering method. The method comprises the following steps: for a graph data set D to be processed, obtaining representative subgraph patternsof all subgraphs in the graph data set D through a depth-first search algorithm; for the representative subgraph patterns, obtaining Top-k diversity subgraph patterns of the graph data set D, and enabling all of the Top-k diversity subgraph patterns to be generated into a Top-k diversity subgraph pattern set RS; carrying out projection matching on each subgraph in the graph data set D and each characteristic sub-graph in the Top-k diversity subgraph pattern set RS and obtaining a characteristic matrix of the graph data set D; and with adaptive entropy serving as a clustering objective function, carrying out clustering processing on the characteristic matrix through a graph projection clustering algorithm to obtain a clustering result. The method enables the clustering result of the graphdata set to be more accurate and higher in diversity; and high-dimensional data processing effect is better.

Description

technical field [0001] The present invention relates to graph data mining technology, in particular to a graph data-oriented projection clustering method. Background technique [0002] Now a large amount of data is emerging in various fields of social life. As a data structure, graph can represent a lot of structurable information and data in social life. For example, in biological information, graphs are used to describe the composition and structure of compounds, combined with data mining technology to predict and judge diseases such as cancer, HIV, and hemophilia; Registered users, use edges to represent the relationship between two users, and use data mining to meet people's various information needs. At present, mining frequent subgraphs is the basis of other operations on graphs, and many methods for mining frequent subgraphs have been proposed. On the basis of frequent subgraph mining, various mining techniques can be used to mine the information that users want. ...

AI Technical Summary

Problems solved by technology

Affected by dimensionality effects, traditional clustering methods show low efficiency and decreased accuracy when dealing with high-dimensional data.

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