Improvement of spatial data clustering algorithm in city location

Qibing Zhu
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引用次数: 2

Abstract

Spatial data mining is a new research direction in the field of Data Mining. In recent years, with the continuous development of data mining technology, spatial data attracts more and more attentions of scholars and experts. Spatial clustering analysis is an important part of spatial data mining. Nowadays, spatial clustering analysis has become more and more mature, widely used in various fields. Spatial clustering analysis algorithm can deeply discover the knowledge which hidden in the geospatial information, find out the representative node of one or a number of spatial data collection, discovery the law of the spatial distribution. Classic clustering algorithm basing on partition widely used in the field of cities planning and provide valuable reference. This paper is based on the spatial data mining method, analysis and optimize the spatial data clustering algorithm in the Location Problem in the city, providing scientific location decisions.
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城市定位空间数据聚类算法的改进
空间数据挖掘是数据挖掘领域的一个新的研究方向。近年来,随着数据挖掘技术的不断发展,空间数据越来越受到学者和专家的关注。空间聚类分析是空间数据挖掘的重要组成部分。如今,空间聚类分析已经越来越成熟,广泛应用于各个领域。空间聚类分析算法可以深入发现隐藏在地理空间信息中的知识,找出一个或多个空间数据集合的代表性节点,发现空间分布的规律。经典的基于分区的聚类算法在城市规划领域得到了广泛的应用并提供了有价值的参考。本文基于空间数据挖掘方法,对城市选址问题中的空间数据聚类算法进行分析和优化,为科学的选址决策提供依据。
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