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Clustering method based on ecological niche genetic algorithm with diverse radius technology

A technology of diversified radius and clustering method, applied in the field of data mining, can solve problems such as poor clustering effect and poor stability

Inactive Publication Date: 2014-12-24
ZHEJIANG UNIV OF TECH
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AI Technical Summary

Problems solved by technology

[0015] In order to overcome the disadvantages of poor clustering effect and poor stability of existing clustering methods based on genetic algorithms, the present invention provides a clustering method based on a variety radius technology with good clustering effect and good stability. Clustering Method of Niche Genetic Algorithm

Method used

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  • Clustering method based on ecological niche genetic algorithm with diverse radius technology
  • Clustering method based on ecological niche genetic algorithm with diverse radius technology
  • Clustering method based on ecological niche genetic algorithm with diverse radius technology

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Embodiment Construction

[0091] The present invention will be further described below in conjunction with the accompanying drawings.

[0092] refer to figure 1 and figure 2 , a clustering method based on a niche genetic algorithm with diversification radius technology, the clustering method includes the following steps:

[0093] 1) Chromosome encoding and population initialization

[0094] A chromosome is encoded as a clustering center, each chromosome is composed of v real numbers, and the chromosome is expressed as c=[c 1 ,c 2 ,...,c v ], where v represents the dimension of the feature space;

[0095] Randomly select N data points, N is the population size, each data point is composed of v-dimensional real numbers, each data point represents a chromosome and there is no repeated data point;

[0096] 2) Calculation of individual fitness

[0097] Let X={x 1 ,x 2 ,...,x n} is a subset of N-dimensional vector space, K is the number of clusters, S(x j ,c i ) represents the data point X j and ...

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Abstract

The invention discloses a clustering method based on an ecological niche genetic algorithm with a diverse radius technology. The clustering method based on the ecological niche genetic algorithm with the diverse radius technology comprises the following steps that (1) chromosome coding and population are initialized; (2) the individual fitness is calculated; (3) the position, content and number of the ecological niches in the population are identified by adopting a dynamic identification method; (4) the radius information of each ecological niche is adjusted by executing the diverse radius mechanism; (5) the new individual fitness is recalculated by applying a fitness sharing function; (6) selection, intersection and mutation operations are executed; (7) an elite strategy is executed to replace the worst individual in the population; (8) if a termination condition is met, the operation is terminated, otherwise the step (5) is executed. The clustering method based on the ecological niche genetic algorithm with the diverse radius technology has the advantages that the clustering effect is good, and the stability is good.

Description

technical field [0001] The invention relates to data mining technology, especially a clustering method. Background technique [0002] Data mining is the process of discovering hidden, undiscovered, but possibly useful information and knowledge from massive, fuzzy, noisy, random, and incomplete data. Clustering analysis is an important content and one of the basic forms of data mining. Data clustering refers to dividing the data into several clusters according to the inherent characteristics of the data, so that the data in each cluster has similar characteristics. Characteristics, the characteristics of data between different aggregation classes have as large a difference as possible. [0003] For the actual data set to be solved, when performing cluster analysis, which or which type of clustering algorithm should be selected, mainly considering the type characteristics of the data, the characteristics of the algorithm, and several factors of the clustering goal. Sometimes...

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Application Information

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IPC IPC(8): G06F17/30G06N3/12
CPCG06F16/35G06F16/285G06N3/12
Inventor 盛伟国范东成汪晓妍李军伟何俊丽陈胜勇
Owner ZHEJIANG UNIV OF TECH
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