dendrogram

简明释义

英[/ˈdɛn.drə.ɡrɑːm/]美[/ˈdɛn.drə.ɡrɑːm/]

n. [生物] 系统树图（表示亲缘关系的树状图解）

英英释义

单词用法

同义词

反义词

例句

1.To contrast veracity and relativity of identification of micrococcaceae by the Dendrogram Herarchy Process Analytic(DHPA)identification method and ATB method.

对比分析双歧层次鉴定法（DHPA法）与ATB法对微球菌鉴定的准确性与可比性。

2.The dendrogram was constructed from the agarose gel data, while the information of alleles was from PAGE gel.

有关等位基因的相关信息从PAGE胶上获得，聚类图构建的数据则来源于琼脂糖凝胶电泳。

3.Based on the ISSR band data, similarity indices and UPGMA dendrogram were analysed and developed.

基于ISSR条带统计数据，运用相关软件进行了相似系数的分析和系统进化树的构建。

4.A dendrogram was drawn by adopting the nearest distance method of clustering in numerical taxonomy.

采用最短距离法对所研究鸡种进行了聚类分析。

5.The molecular dendrogram among goat populations was in accord with their genetic relationship.

山羊群体间的分子聚类关系与各群体间的亲缘关系基本一致。

6.Putong peach had higher genetic similarity and frequency of gene exchange by dendrogram of RAPD bands.

从RAPD带型聚类图分析，普通桃类的遗传相似系数较高，基因交换频繁。

7.The positions of the mulberry wild silkworm from Shaanxi were very peculiar in the molecular dendrogram, suggesting the importance of this region in the evolution and diversification of this specics.

遗传距离和系统发育分析结果显示陕西一带的野桑蚕成分复杂，有重要的演化意义。

8.In biology, a dendrogram 树状图 is often used to illustrate the evolutionary relationships between species.

在生物学中，树状图常用于说明物种之间的进化关系。

9.The researchers created a dendrogram 树状图 to visualize the clustering of different genetic samples.

研究人员创建了一个树状图以可视化不同基因样本的聚类。

10.During the data analysis, we generated a dendrogram 树状图 to help identify patterns in the dataset.

在数据分析过程中，我们生成了一个树状图以帮助识别数据集中的模式。

11.The dendrogram 树状图 displayed the hierarchical structure of the organization clearly.

该树状图清晰地展示了组织的层级结构。

12.To better understand the relationships among various algorithms, we constructed a dendrogram 树状图 based on their performance metrics.

为了更好地理解各种算法之间的关系，我们根据它们的性能指标构建了一个树状图。

作文

In the field of data analysis and statistics, visual representation of complex data is crucial for understanding relationships among various components. One such tool that has gained popularity is the dendrogram. A dendrogram is a tree-like diagram that illustrates the arrangement of the clusters formed through hierarchical clustering. It provides a visual summary of the data's structure and helps in identifying patterns and similarities among different groups. The concept of a dendrogram is particularly useful in biology, where researchers often need to analyze genetic similarities among species or individuals. For instance, when studying evolutionary relationships, a dendrogram can depict how closely related different organisms are based on their genetic information. By observing the branches of the dendrogram, scientists can infer evolutionary paths and understand how certain traits have been inherited over time. In addition to biology, dendrograms are also widely used in social sciences, marketing research, and even machine learning. In social sciences, researchers may use a dendrogram to cluster survey responses and identify distinct groups within a population. This technique allows for better targeting of marketing strategies by understanding consumer behavior and preferences. For example, a company might analyze customer feedback using a dendrogram to determine which segments of their audience share similar opinions about a product. Creating a dendrogram involves several steps, including selecting an appropriate distance metric to measure similarity between data points and choosing a linkage method to determine how clusters are formed. Common distance metrics include Euclidean distance and Manhattan distance, while common linkage methods include single-linkage, complete-linkage, and average-linkage clustering. The choice of these parameters can significantly influence the resulting dendrogram, making it essential for analysts to carefully consider their options. Once the dendrogram is constructed, it can be interpreted to reveal insights about the data. The height of the branches in the dendrogram indicates the level of similarity between clusters; shorter branches suggest that the clusters are more closely related, while longer branches indicate greater dissimilarity. This visual representation allows researchers to make informed decisions about how to group or classify their data. However, while dendrograms are powerful tools for data visualization, they also come with limitations. One major drawback is that they can become complex and difficult to interpret when dealing with large datasets. As the number of data points increases, the dendrogram can become cluttered, making it challenging to discern meaningful patterns. Therefore, it is often necessary to apply data reduction techniques before constructing a dendrogram to ensure clarity and accuracy. In conclusion, the dendrogram is an invaluable tool in data analysis, providing a clear and concise way to visualize the relationships among data points. Whether in biology, social sciences, or marketing, the ability to cluster and analyze data through a dendrogram opens up new avenues for research and decision-making. As we continue to generate vast amounts of data, mastering the use of dendrograms will be essential for extracting meaningful insights and driving progress in various fields of study. Overall, understanding the function and application of a dendrogram can enhance our analytical capabilities and improve our ability to interpret complex data effectively.

在数据分析和统计学领域，复杂数据的可视化表示对于理解各个组成部分之间的关系至关重要。其中一种逐渐受到欢迎的工具是dendrogram。dendrogram是一种树状图，展示了通过层次聚类形成的簇的排列。它提供了数据结构的可视化摘要，并有助于识别不同组之间的模式和相似性。 dendrogram的概念在生物学中尤其有用，研究人员常常需要分析物种或个体之间的遗传相似性。例如，在研究进化关系时，dendrogram可以描绘不同生物基于其遗传信息的亲缘关系。通过观察dendrogram的分支，科学家可以推断进化路径，并了解某些特征如何随着时间的推移被遗传。 除了生物学，dendrogram还广泛应用于社会科学、市场研究甚至机器学习。在社会科学中，研究人员可能使用dendrogram对调查响应进行聚类，以识别群体中的不同组。这种技术通过了解消费者行为和偏好来更好地针对市场营销策略。例如，一家公司可能会使用dendrogram分析客户反馈，以确定其受众中的哪些细分群体对产品有相似的看法。 创建dendrogram涉及几个步骤，包括选择合适的距离度量来测量数据点之间的相似性，以及选择链接方法来确定如何形成簇。常见的距离度量包括欧几里得距离和曼哈顿距离，而常见的链接方法包括单链接、完全链接和平均链接聚类。这些参数的选择会显著影响生成的dendrogram，因此分析师必须仔细考虑他们的选择。 一旦构建了dendrogram，就可以对其进行解释，以揭示有关数据的见解。dendrogram中分支的高度表示簇之间的相似水平；较短的分支表明簇之间的关系更为密切，而较长的分支则表示差异更大。这种可视化表示使研究人员能够就如何分组或分类数据做出明智的决策。 然而，尽管dendrogram是强大的数据可视化工具，但它们也有局限性。一个主要缺点是，当处理大型数据集时，它们可能变得复杂且难以解释。随着数据点数量的增加，dendrogram可能会变得杂乱，使得辨别有意义的模式变得具有挑战性。因此，通常需要在构建dendrogram之前应用数据减少技术，以确保清晰和准确。 总之，dendrogram是数据分析中不可或缺的工具，提供了一种清晰简洁的方式来可视化数据点之间的关系。无论是在生物学、社会科学还是市场营销，通过dendrogram对数据进行聚类和分析的能力为研究和决策开辟了新的途径。随着我们继续生成大量数据，掌握dendrogram的使用将对提取有意义的见解和推动各个研究领域的进步至关重要。 总体而言，理解dendrogram的功能和应用可以增强我们的分析能力，提高我们有效解释复杂数据的能力。