Classified variable clustering method based on attribute weight similarity

Abstract

The invention discloses a classified variable clustering method based on the attribute weight similarity. On the basis of the attribute weight similarity, the clustering process is converted into a process of searching for graph connected components, data points of data concentration serve as nodes, when the attribute weight similarity of two data points of data concentration is larger than or equal to theta, it is considered that one connecting line exists between the two data points (the parameter theta is given in advance), and when the attribute weight similarity of two data points of data concentration is smaller than theta, it is considered that no connecting line exists between the two data points. After an undirected graph is determined, each connected component of the undirected graph is a cluster, and the records in the clusters are peaks of the connected components. The classified variable clustering method based on the attribute weight similarity is substantially used for searching for the peaks contained in the connected components of the undirected graph, the clustering process can be guided through the thought of a graph traversal algorithm, and therefore the time and space complexity is low, and the clustering result is high in accuracy.

Description

technical field [0001] The invention belongs to the technical field of computer data processing methods, and relates to a classification variable clustering method based on attribute weight similarity. Background technique [0002] Clustering is an important research topic in data mining. Early clustering methods used distance to measure the dissimilarity between two records, such as k-means, DBSCAN and other methods. For categorical variable data sets, existing standardization methods can be used to convert them into interval scale variables, so that traditional methods can be used for clustering. However, there is usually no quantitative relationship between the attribute values ​​of categorical variables, so the standardization work has a lot of blindness. Therefore, using traditional methods to deal with categorical variables will affect the clustering effect. [0003] The ROCK clustering method proposed by Guha S et al. introduces the concept of link. The introduction...

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Problems solved by technology

However, this method is generally slower than ROCK due to the fact that more neighbors are considered during the execution of DNNS.

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