Data stream adaptive clustering method for mixed attributes

Abstract

The invention discloses a data stream adaptive clustering method for mixed attributes. The method comprises the following steps: 1) data are pretreated and a mesh is initialized, and the partition granularity of the mesh in attributes per dimension and the similarity between mesh objects during an offline clustering process are determined; 2) online maintenance is carried out on the mesh; 3) when a user sends a clustering request, the clustering process is changed to an offline stage from an online stage, the mesh is divided to a dense mesh and a sparse mesh according to the density information of the mesh, as for the dense mesh, an improved DBSCAN algorithm is used for clustering, as for the sparse mesh, a density-distance distribution-based CCFD algorithm is used for clustering, and the finally-obtained clustering result is outputted; and the whole clustering process is completed. Applicability and scalability are good, a related data set can be effectively processed, and a good clustering result is obtained.

Description

technical field [0001] The invention belongs to the field of clustering methods, and relates to a data flow clustering method oriented to mixed attributes. Background technique [0002] With the development of big data technology, the amount of data generated has increased rapidly, and cluster analysis, as an important technology for the analysis of various data, has once again become a research hotspot. Cluster analysis is widely used in various fields such as finance, marketing, information retrieval, information filtering, scientific observation and engineering. Mixed attribute data stream clustering is aimed at mixed attribute data streams. The original data arrives in the form of a huge data stream. Most of the attributes of the data stream have both numerical attributes with continuous values ​​and classification attributes representing categories or states. Mixed attributes of attribute types require preprocessing, clustering and knowledge extraction on the data stre...

AI Technical Summary

Problems solved by technology

[0006] In order to overcome the deficiencies of most existing data stream clustering methods, the cluster centers need to be manually determined, the clustering accuracy is low, the mixed attribute data sets cannot be effectively processed, the clustering effects of different data sets are very different, and the parameter dependence is large. The present invention provides a mixed-attribute-oriented data flow adaptive clustering method, which has the characteristics of being able to process mixed-attribute data sets, fast processing speed and high accuracy

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