Radviz-based fuzzy clustering result visualization method

Abstract

The present invention provides a Radviz-based fuzzy clustering result visualization method, comprising the steps of 1) performing data pre-processing on a result of a fuzzy clustering algorithm; 2) designing a reasonable clustering cluster dimension anchor point layout for a RadViz circumference; 3) projecting data samples inside the Radviz in a round dot mode or a pie chart mode; 4) extending dimension anchor points to dimension circular rings, and achieving visual encoding of the Radviz circular rings; 5) fusing membership degree distribution information to a Radviz front view; and 6) extracting coexistence and association relations among clusters, and mapping the coexistence relations by using chord lines. A fuzzy membership degree matrix obtained by the fuzzy clustering algorithm is displayed based on Radviz, as much fuzzy clustering information as possible is provided for users, researchers are allowed to freely explore information such as data attributes, membership degree matrixes and clustering clusters, and therefore the users can make a decision more rapidly, visually and accurately.

Description

technical field [0001] The invention belongs to the technical field of computer information processing, and relates to a visualization method of fuzzy clustering results based on Radviz. Background technique [0002] Fuzzy clustering is an overlapping clustering method that allows data objects to belong to more than one cluster. In fuzzy clustering, data points correspond to a degree of membership on each cluster, which reflects the degree of uncertainty that the data point belongs to the category, and the result of fuzzy clustering is that the data point belongs to each cluster membership matrix. Since it is difficult to accurately determine the boundaries of clusters in the real world, the partial division of fuzzy clustering is more reasonable and can more objectively reflect the real world. However, when the clustering algorithm obtains many clusters and the data contains a large number of nodes, the membership matrix is ​​a high-dimensional data with a complex structu...

AI Technical Summary

Problems solved by technology

There are some inherent defects in the process of transforming fuzzy membership into hard judgment, such as too much information loss, relatively large errors may occur, and even the opposite result is obtained.

[0004] On the other hand, the membership degree matrix stores the membership degree of the data points divided into each cluster, which can help us intuitively judge which cluster the data point should be divided into relatively speaking, but it does not allow us to obtain more More meaningful information, such as the relationship between multiple clusters, the size of each cluster, etc.

[0005] These problems will make it difficult for users to fully and accurately understand the results of fuzzy clustering, and to some extent weaken the ease of use of fuzzy clustering analysis in practical applications.

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