Graph-data-oriented projection clustering method

A clustering method, a technology of graph data, applied in the fields of instrument, calculation, character and pattern recognition, etc., can solve problems such as low efficiency and decreased accuracy

Inactive Publication Date: 2018-05-25
NORTHEASTERN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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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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  • Graph-data-oriented projection clustering method
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  • Graph-data-oriented projection clustering method

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Embodiment Construction

[0085] In order to better explain the present invention and facilitate understanding, the present invention will be described in detail below in conjunction with the accompanying drawings and through specific embodiments.

[0086] In the following description, various aspects of the present invention will be described. However, those skilled in the art can implement the present invention by using only some or all of the structures or processes of the present invention. For clarity of explanation, specific numbers, arrangements and sequences are set forth, but it will be apparent that the invention may be practiced without these specific details. In other instances, well-known features have not been described in detail in order not to obscure the invention.

[0087] The core idea in the embodiment of the present invention is: first perform diversity feature subgraph (Top-k subgraph mode) mining on the graph data set, and then use the mined diversity feature subgraph to represen...

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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. ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/2323
Inventor 印莹赵宇海梁燕曹丽蒙张斌
Owner NORTHEASTERN UNIV
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