High-dimensional data hypergraph model construction method based on feature induction

Abstract

The present invention discloses a high-dimensional data hypergraph model construction method based on feature induction. The method comprises the following steps: discretizing t attribute values of n data records of a high-dimensional data set D, using one data record as one row of an initial matrix X, and using the discretized attribute values of the data record as a column, thereby obtaining the initial matrix X; under the non-negative condition, initializing a high-dimensional data set feature-based matrix U and a high-dimensional data set feature coefficient matrix V; performing repeated iteration on the U and V by using an iteration function to obtain an approximate solution until a value of an objective function Q(X, U, V) is reduced to a set threshold, thereby obtaining a matrix U' with a reduced scale; and considering each row of the matrix U' as a data record, defining different attribute values as nodes of a hypergraph, and constructing one hyperedge of the hypergraph with each row of the matrix U', thereby obtaining the hypergraph G. The high-dimensional data hypergraph model construction method based on feature induction is capable of performing complete cluster analysis on a high-dimensional data set, and can further improve the operation efficiency of a high-dimensional data clustering algorithm.

Description

technical field [0001] The invention relates to a high-dimensional data clustering algorithm, in particular to a method for constructing a high-dimensional data hypergraph model based on feature induction. Background technique [0002] With the development of society, the amount of data has expanded rapidly, and the timeliness and complexity of data have far exceeded the current information processing capabilities. "Informatization" and "globalization" have become two important features of the 21st century. Driven by network technology, people's ability to produce and collect data has greatly improved in the past ten years, and the ability to obtain and produce data has greatly exceeded the ability to process data. Today, when data production and transmission capabilities are far greater than data analysis capabilities, although people are overwhelmed by data, they are hungry for knowledge. So data mining and knowledge discovery technology came into being and developed vig...

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Problems solved by technology

Therefore, it is impossible to comprehensively perform cluster analysis on high-dimensional data sets.

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