Webpage clustering method based on node property label propagation

Abstract

The invention provides a webpage clustering method based on node property label propagation, which comprises the following steps: structuring a topological structure diagram model according to the link relationship of the webpage; building property feature vector for each node in the diagram model; initializing node labels in the network; iteratively updating the node labels according to the topological structure and the node property; and defining the condition of stopping updating the node labels. With the method, effective and high-quality webpage clustering can be realized within the nearly linear time only by extracting the topological relationship of the internet and the property information marking webpage characteristics without knowing priori knowledge such as the quantity and the scale of webpage groups and the like or predefining parameters such as the critical value and the like. The algorithm is simple in concept, easy to understand and realize and lower in time complexity, and can generate high-quality network clustering result, so as to apply to large-scale internet.

Description

technical field [0001] The invention relates to a web page clustering method. Background technique [0002] With the continuous in-depth exploration of data mining theory, complex network analysis has attracted the attention of researchers in many fields. Complex network clustering has become the focus of research interests of some domestic and foreign scholars. Many scholars have devoted themselves to the study of clustering techniques in complex networks, and they discovered network cluster structures by using different methods. [0003] Nodes in the network may have different levels of organizational structure, such as a large cluster structure may contain small cluster structures. Hierarchical clustering method is a traditional method of clustering, including top-down split hierarchical clustering and bottom-up agglomerative hierarchical clustering. The representative algorithms are the article Community in PNAS.2002,99(12). The GN algorithm published in structure in ...

AI Technical Summary

Problems solved by technology

With the continuous development of Internet technology and the continuous increase of data scale, the above-mentioned algorithms generally have high time complexity, and the mining of all cluster structures in the network will take a lot of time, which is not very good for large-scale networks. Be applicable

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