| 基于K-means的机构归一化研究 |
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| 引用本文： | 孙海霞,李军莲,吴英杰.基于K-means的机构归一化研究[J].医学信息学杂志,2013,34(7):41-44. |
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| 作者姓名： | 孙海霞 李军莲 吴英杰 |
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| 作者单位： | 中国医学科学院医学信息研究所 北京100020 |
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| 基金项目： | 国家“十二五”科技支撑计划项目“科技知识组织体系的协同工作系统和辅助工具开发”（项目编号：2011BAH10B02） |
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| 摘 要： | 分析k-means算法的核心思想和基本步骤,借鉴现有基于频繁词集的文本聚类初始中心确定方法,提出一种面向大规模机构名称归一化处理应用的机构聚类方法,详细阐述机构聚类中心的生成、相似度算法的选择以及迭代次数问题,其实验和应用效果表现良好.
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| 关 键 词： | 机构归一 机构聚类 K-means 频繁词集 相似度计算 |
| 收稿时间： | 1/8/2013 12:00:00 AM |
Research on Institutions Normalization Based on K-means |
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| SUN Hai-xi,LI Jun-lian,WU Ying-jie.Research on Institutions Normalization Based on K-means[J].Journal of Medical Informatics,2013,34(7):41-44. |
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| Authors: | SUN Hai-xi LI Jun-lian WU Ying-jie |
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| Institution: | Institute of Medical Information & Library of Chinese Academy of Medical Sciences |
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| Abstract: | The paper analyzes the core idea and basic steps of k-means, learns from the existing methods of determining initial text cluster centers which is based on frequent word sets, proposes a practical institutions cluster method meeting the need of large-scale institutions processing applications. It concretely elaborates the generation of institution clustering center, the selection of similarity algorithm and iterative times, the experimental results and its application perform well. |
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| Keywords: | Institutions normalization Institutions cluster K-means Frequent word sets Similarity computation |
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