Data clustering method for rapidly determining clustering center

Abstract

Provided is a data clustering method for rapidly determining a clustering center, comprising the steps of: 1) reading an original data set, selecting a corresponding distance calculating method through dominance analysis, and solving the distance matrix of a whole data set; 2) rapidly determining a clustering center; and 3) selecting optimal dc: 3.1, finding the maximum value dmax and the minimum value dmin in a similarity distance matrix, and calculating a current dc value through setting a percent value; 3.2, after dc is selected and a clustering result is obtained, designing a Fitness function as an evaluation index; 3.3, employing a climbing algorithm to select optimal dc; and 3.4, outputting the optimal dc and the clustering result of the optimal dc. The data clustering method possesses the characteristics of higher accuracy, and smaller difference and parameter dependency of different data set clustering effects.

Description

technical field [0001] The invention belongs to data analysis technology, in particular to a data clustering method. Background technique [0002] With the development of big data technology, the amount of data generated has increased rapidly, and cluster analysis, as an important technology for the analysis of various data, has once again become a research hotspot. Cluster analysis is widely used in various fields such as finance, marketing, information retrieval, information filtering, scientific observation and engineering. Traditional clustering algorithms include: partition-based algorithms, hierarchy-based algorithms, density-based algorithms, etc. [0003] Algorithms based on partitioning include k-means algorithm and PAM algorithm. The similarity calculation of the k-means algorithm is based on the average value of objects in a cluster. The goal of the algorithm is to divide the data set into k clusters according to the input parameter k. The algorithm adopts an ...

AI Technical Summary

Problems solved by technology

[0006] Aiming at the shortcomings of the existing data clustering methods that the cluster centers need to be determined manually, the clustering accuracy rate is low, the clustering effects of different data sets are different and the parameter dependence is large, the present invention proposes a density-based clustering method Data clustering method for fast center determination, high accuracy, less difference in clustering effects of different data sets and less parameter dependence

Images