High-dimensional data clustering method based on unweighted hypergraph segmentation

Abstract

The invention discloses a high-dimensional data clustering method based on unweighted hypergraph segmentation. The high-dimensional data clustering method comprises the steps of: mapping different attribute values in a high-dimensional data set as nodes of an unweighted hypergraph, and mappting each data record as a hyperedge of the unweighted hypergraph to obtain an original unweighted hypergraph, wherein each node contained in the hyperedge is an attribute value of the data record; segmenting the original unweighted hypergraph into k parts, and regarding each part as a clustering subgraph; and evaluating quality of a clustering result by using compactness of the clustering subgraphs, wherein the greater the compactness, the better the quality of the clustering result, the compactness of the clustering subgraphs is defined by a ratio of v1 to v2, the v1 is the number of nodes simultaneously occupied by at least two hyperedges among all the nodes in the clustering subgraphs, and the v2 is the number of nodes occupied by only one hyperedge. The high-dimensional data clustering method can be used for carrying out clustering analysis on the high-dimensional data set comprehensively, and can further improve the calculating efficiency of a high-dimensional data clustering algorithm.

Description

technical field [0001] The invention belongs to the technical field of high-dimensional data clustering processing, and in particular relates to a high-dimensional data clustering method based on unweighted hypergraph segmentation. Background technique [0002] With the rapid development of information technology, the amount of data accumulated by people has increased rapidly, often forming many high-dimensional data sets. How to extract useful knowledge from massive high-dimensional data sets has become a top priority for researchers. Data mining is a data processing technology developed to meet this need. Data mining includes various methods such as association rules, clustering, classification, etc. In recent years, the clustering algorithm for high-dimensional data has gradually become a research hotspot. One of its typical applications is the forecast of the stock market. By clustering and analyzing the high-dimensional data sets of the stock market, a collection of s...

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Problems solved by technology

But the disadvantage is that the hypergraph cannot reflect the data distribution of the entire data set, and cannot complete the cluster analysis of all data

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