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Using Discriminant Analysis to Verify the Clustering of Self-Organizing Map

机译：使用判别分析验证自组织图的聚类

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摘要

The data models according to the hot spots spreading in Indonesian forests are usually available with the large of feature space and heterogeneous of distribution patterns. The complexities of this hot spot data structure are central to the present analysis. Clustering of the hot spot regions that persist over time are good indicators of fire risk problems. Therefore, the self-organizing map (SOM) was implemented for clustering hot spot regions. This method is a nonlinear statistical technique that can be used for solving data problems that involved classification and information visualization. The finding of study shows that SOM has provided a classification of hot spot via regions into some different clusters. However, a specification of the cluster is needed when the SOM nodes does not clearly reveal the borders of cluster. Under these circumstances, a supervised learning of discriminant analysis (DA) is used to validate the SOM clusters. The main purpose of DA is to predict cluster membership according to a given prior cluster information, through distance measures and distinct coloring of the nodes in the SOM. DA gave highly accurate cluster discrimination, which shows that this method can be a useful tool to verify the SOM clustering. The combination of the proposed methods is a reliable means of classifying and visualizing of the data, and enables interpretation of the disparities of fire risk by regions in forest on the basis of the hot spot data.
机译：通常根据印度尼西亚森林中分布的热点的数据模型具有较大的特征空间和分布模式的异质性。这种热点数据结构的复杂性是当前分析的核心。随着时间推移而持续存在的热点地区的聚集是火灾风险问题的良好指标。因此，实施了自组织地图（SOM）以对热点区域进行聚类。此方法是一种非线性统计技术，可用于解决涉及分类和信息可视化的数据问题。研究发现表明，SOM通过区域将热点分类为一些不同的群集。但是，当SOM节点不能清楚地显示群集的边界时，需要对群集进行规范。在这种情况下，使用判别分析（DA）的监督学习来验证SOM群集。 DA的主要目的是通过距离度量和SOM中节点的不同颜色来根据给定的先前群集信息预测群集成员。 DA提供了高度准确的聚类判别，这表明该方法可以作为验证SOM聚类的有用工具。所提出的方法的组合是对数据进行分类和可视化的可靠方法，并且能够根据热点数据来按森林区域解释火灾风险的差异。

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