Fuzzy entropy space clustering analysis method based on orderly information entropy

Abstract

The invention discloses a fuzzy entropy space clustering analysis method based on orderly information entropy, comprising the following steps: S1, inputting a standardized matrix, and letting an integer i=1; S2, assigning an ith value corresponding to a relationship set D to a fuzzy entropy space parameter, and initializing intermediate sets A and C; S3, letting an intermediate set B be empty; S4, if R(xj, xk)>=lambda, deciding that B=B union {xj} and A=A\{xj}; S5, if R(xj, xk)>=lambda, deciding that B=B union {xs} and A=A\{xs}, or going to S6; S6, letting C=C union {B}, and letting an integer i+1 assign a value to i; repeating the steps S2 to S6 until the integer i is equal to the size m of the relationship set D; S7, if A is empty, deciding that X(lambda)=C, and calculating the orderly information entropy at the moment; S8, if HP(min)=min{X(lambda)}, deciding that min=lambda and going to S9, or going to S2; and S9, outputting an optimal granularity level.

Description

technical field [0001] The invention relates to the field of cluster analysis, in particular to a fuzzy entropy space cluster analysis method based on ordered information entropy. Background technique [0002] Clustering, as an ancient problem, continues to deepen with the emergence and development of human society. Cluster analysis is an important research field in the field of machine learning. It is an important method to study the regularity between discrete data. Clustering has a wide range of applications in many fields, including pattern recognition, data mining, and market research. The goal of clustering is to gather data into different classes without any prior knowledge, discover the most essential properties between sample points, make the difference between classes as large as possible, and the difference within a class as small as possible. People have done various researches on clustering for a long time, and the traditional clustering algorithms can be rou...

AI Technical Summary

Problems solved by technology

The existing technologies mainly include partition clustering, hierarchical clustering, density-based clustering and grid-based clustering, but most fuzzy clustering requires artificially specifying the number of clusters

The initial clustering center is sensitive to initialization and it is difficult to obtain the global optimum

Moreover, the current clustering algorithm based on the fuzzy quotient space obtains a hierarchical clustering result, but it is impossible to know which granularity level is the best clustering result

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