dendrograms

简明释义

英[ˈdɛndrɒɡrəm]美[ˈdɛn.drə.ɡræm]

[生物]系统树图（dendrogram 的复数）

英英释义

单词用法

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反义词

例句

1.The genetic relationships among the varieties were evaluated by dendrograms and Multidimensional Scaling (MDS) , showing that the tested poplar can be classified into five groups .

聚类分析和多维尺度分析将试验材料大体上分为五类， 结果不仅显示了组间不同品种的差异， 而且大体上区分了我国原生品种和外来品种。

2.The cluster analysis is one of the most commonly_used multivariable statistical methods whose results are often expressed with the dendrograms.

聚类分析是最常用的多元统计方法之一，其结果常用谱系图来表示。

3.We analyzed the dendrograms between lignin-degrading enzyme activities and genes, we found the results were not completely consistent, but still had relevance.

分别对四个地区木蹄层孔菌木质素酶活性和遗传关系进行聚类分析，结果并不完全一致，但具有一定的相关性。

4.The genetic relationships among the varieties were evaluated by dendrograms and Multidimensional Scaling (MDS) , showing that the tested poplar can be classified into five groups .

聚类分析和多维尺度分析将试验材料大体上分为五类， 结果不仅显示了组间不同品种的差异， 而且大体上区分了我国原生品种和外来品种。

5.The researchers used dendrograms to visualize the genetic relationships between different species.

研究人员使用dendrograms来可视化不同物种之间的遗传关系。

6.The biologist presented dendrograms to illustrate the evolutionary history of the organisms.

生物学家展示了dendrograms以说明生物的进化历史。

7.The software generates dendrograms automatically based on the input data.

该软件根据输入数据自动生成dendrograms。

8.In the study of clustering algorithms, dendrograms are essential for understanding data groupings.

在聚类算法的研究中，dendrograms对于理解数据分组至关重要。

9.By analyzing dendrograms, we can determine which species share a more recent common ancestor.

通过分析dendrograms，我们可以确定哪些物种共享较近的共同祖先。

作文

In the field of data analysis and statistics, one often encounters various tools and methods to visualize complex relationships within data sets. One such powerful tool is the dendrograms, which serves as a visual representation of hierarchical clustering. A dendrogram is essentially a tree-like diagram that illustrates the arrangement of the clusters formed by the hierarchical clustering algorithm. This technique is particularly useful when dealing with large datasets where understanding the relationships between different groups can be challenging. The construction of a dendrogram begins with the calculation of distances or similarities between data points. Each data point is initially treated as a separate cluster. As the algorithm progresses, pairs of clusters are merged based on their proximity until all points are combined into a single cluster. The dendrogram visually captures this process, allowing researchers to observe how clusters form and the order in which they merge. One of the key advantages of using dendrograms is that they provide insights into the structure of the data. By examining the branches of the dendrogram, one can determine the number of clusters present and identify outliers or anomalies within the data. For instance, if a particular branch is significantly longer than others, it may indicate that the corresponding cluster is distinct from the rest, warranting further investigation. Moreover, dendrograms can be utilized in various fields, including biology, marketing, and social sciences. In biology, for example, dendrograms are frequently employed to illustrate evolutionary relationships among species. By analyzing genetic data, scientists can create a dendrogram that depicts how closely related different organisms are, helping to trace their evolutionary history. Similarly, marketers can use dendrograms to segment customers based on purchasing behavior, enabling targeted marketing strategies. While dendrograms are incredibly useful, it is essential to interpret them cautiously. The choice of distance metric and clustering method can significantly impact the resulting dendrogram. Different algorithms may yield different structures, leading to varied interpretations of the same data. Therefore, researchers must carefully select the appropriate methods and validate their findings through additional analyses. In conclusion, dendrograms are invaluable tools in data analysis that facilitate the understanding of complex relationships within datasets. They provide a clear visual representation of hierarchical clustering, allowing researchers to identify patterns, outliers, and the overall structure of the data. As we continue to generate vast amounts of data in various fields, the importance of tools like dendrograms will only grow, aiding in our quest to make sense of the intricate web of information that surrounds us. By mastering the use of dendrograms, researchers can enhance their analytical capabilities and uncover deeper insights into their data.

在数据分析和统计学领域，人们常常会遇到各种工具和方法来可视化复杂的数据集之间的关系。其中一种强大的工具是dendrograms，它作为层次聚类的可视化表示。dendrogram本质上是一种树状图，展示了由层次聚类算法形成的聚类的排列。这种技术在处理大型数据集时尤其有用，因为理解不同组之间的关系可能具有挑战性。构建dendrogram的过程始于计算数据点之间的距离或相似性。每个数据点最初被视为一个单独的聚类。随着算法的进展，基于它们的接近性，成对聚类被合并，直到所有点合并为一个单一的聚类。dendrogram直观地捕捉了这一过程，使研究人员能够观察聚类是如何形成的以及它们合并的顺序。使用dendrograms的一个主要优势是它们提供了对数据结构的洞察。通过检查dendrogram的分支，可以确定存在的聚类数量，并识别数据中的异常值或离群值。例如，如果某个分支明显比其他分支长，则可能表明相应的聚类与其余部分显著不同，值得进一步调查。此外，dendrograms可以应用于多个领域，包括生物学、市场营销和社会科学。在生物学中，例如，dendrograms常用于说明物种之间的进化关系。通过分析遗传数据，科学家可以创建一个dendrogram，描绘不同生物体之间的亲缘关系，从而帮助追踪它们的进化历史。同样，市场营销人员可以使用dendrograms根据购买行为对客户进行细分，从而实现精准的营销策略。虽然dendrograms非常有用，但必须谨慎解读。距离度量和聚类方法的选择会显著影响结果dendrogram。不同的算法可能会产生不同的结构，从而导致对相同数据的不同解释。因此，研究人员必须仔细选择适当的方法，并通过额外的分析验证他们的发现。总之，dendrograms是数据分析中不可或缺的工具，有助于理解数据集中复杂的关系。它们提供了层次聚类的清晰可视化表示，使研究人员能够识别模式、离群值以及数据的整体结构。随着我们在各个领域继续生成大量数据，像dendrograms这样的工具的重要性只会增加，帮助我们努力理解围绕我们的复杂信息网络。通过掌握dendrograms的使用，研究人员可以增强他们的分析能力，深入挖掘数据中的洞察。