›› 2009, Vol. 21 ›› Issue (3): 0-197.

• 论文 •    

改进模糊聚类算法在浙江旱栽优势作物核心种质研究中的应用

李笑 1,徐志福 1,朱丹华 2   

  1. 1 浙江省农业科学院农村发展与信息研究所,浙江杭州310021;2 浙江省农业科学院作物与核技术利用研究所,浙江杭州310021
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2009-05-25 发布日期:2009-05-25

Improved fuzzy clustering algorithm and its application in study on core collection of dryland cultivation crop in Zhejiang

LI Xiao;XU Zhi—fu;ZHU Dan—hua   

  1. 1 Institute of Rural Development and Infommtion,Zhejiang Academy of Agricultural Sciences,Hangzhou 310021,China;2 Institute of Crop Research and Atomic Technique Utilization,Zhejiang Academy of Agricultural Sciences,Hangzhou 310021,China
  • Received:1900-01-01 Revised:1900-01-01 Online:2009-05-25 Published:2009-05-25

摘要: 模糊聚类分析已被广泛应用在气象预报、地质、模式识别、数据挖掘等方面。文章将模糊聚类分析应用于作物核心种质构建过程中,并对传统的模糊聚类算法进行了改进,在相似系数和距离系数的基础上,提出了一种既能考虑到样本之间的值贴近程度,又能考虑到样本之间的形贴近程度的改进系数——相似度,用相似度矩阵替代传统的相似矩阵,使模糊聚类分析模型能够更符合构建作物核心种质的需要。

关键词: 模糊聚类, 核心种质, 相似度

Abstract: Fuzzy clustering algorithm is widely applied in weather service,geology,pattern recognition and data mining.In this paper,fuzzy clustering was applied in core collection of crop and the traditional fluzzy clustering algorithm was improved.Based on similarity coefficient and distance coefficient, similarity degree has been presented which could describe not only the degree of value similarity,but also the degree of shape similarity.Replaced conventional similarity matrix by similarity degree matrix,the fuzzy clustering algorithm was effective.

Key words: fuzzy clustering, core collection, similarity degree