agglomerative

简明释义

英[əˈɡlɒmərətɪv]美[əˈɡlɑːməˌreɪtɪv]

adj. 会凝聚的；[冶] 烧结的，凝结的

英英释义

单词用法

同义词

反义词

例句

1.We also examine the traditional agglomerative hierarchical clustering methods using the information of content to have a thorough comparison.

我们也检验传统用文章内容为资讯的聚合阶层式分群方法以更全面比较其中差异。

2.Another distinction often made in the literature of numerical taxonomy is that between divisive and agglomerative approaches.

在文献的版值分类方法中，还存在另一个区别，即划分法和聚合法。

3.The effects of macromolecular dispersant and subzero treatment on the preparation of ultrafine non agglomerative zirconia powders have been studied.

研究了高分子分散剂及低温处理在制备无团聚超细氧化锆粉末中的作用。

4.As an agglomerative hierarchical clustering algorithm, CURE firstly employs the method of representing clusters by selecting some "representative points".

CURE算法是一种凝聚的层次聚类算法，它首先提出了使用多代表点描述簇的思想。

5.The agglomerative advantages of industrial clusters manifest realization of regional static efficiency and increase of dynamic progressive ability.

其集聚优势表现为区域静态效率的实现和动态进步能力的提高。

6.As an agglomerative hierarchical clustering algorithm, CURE firstly employs the method of representing clusters by selecting some "representative points".

CURE算法是一种凝聚的层次聚类算法，它首先提出了使用多代表点描述簇的思想。

7.The agglomerative methods of Cluster Analysis and their application in classifying animals are described.

文中介绍了聚类分析中的聚合法及其在动物分类中的应用。

8.In the field of data mining, agglomerative 聚合的 clustering methods are often preferred for their simplicity.

在数据挖掘领域，agglomerative 聚合的聚类方法因其简单性而常被优先选择。

9.The agglomerative 聚合的 process allows for the creation of a hierarchy of clusters.

这种agglomerative 聚合的过程允许创建一个层次化的聚类结构。

10.The agglomerative 聚合的 method can effectively reduce the complexity of large datasets.

这种agglomerative 聚合的方法可以有效降低大型数据集的复杂性。

11.Researchers applied an agglomerative 聚合的 technique to analyze customer behavior patterns.

研究人员应用了一种agglomerative 聚合的技术来分析客户行为模式。

12.The clustering algorithm used in this analysis is based on an agglomerative 聚合的 approach to group similar data points.

本次分析中使用的聚类算法基于一种agglomerative 聚合的方法来对相似的数据点进行分组。

作文

In the field of data science and machine learning, clustering is a fundamental technique used to group similar data points together. One of the most popular clustering methods is known as agglomerative clustering. This method is particularly useful in scenarios where we need to understand the inherent structure of a dataset without prior knowledge of the number of clusters. The term agglomerative refers to a bottom-up approach where each data point starts as its own cluster, and pairs of clusters are merged together based on their similarity until a single cluster remains or until a specified number of clusters is achieved.The process of agglomerative clustering begins with the calculation of a distance matrix that quantifies the similarity between all pairs of data points. Various distance metrics can be used, such as Euclidean distance, Manhattan distance, or cosine similarity, depending on the nature of the data and the specific requirements of the analysis. After establishing this matrix, the algorithm identifies the two closest clusters and merges them into a new cluster. This process is repeated iteratively, leading to larger and larger clusters until the desired number of clusters is reached.One of the advantages of agglomerative clustering is its flexibility. It can be applied to various types of data, including numerical, categorical, and mixed data types. Moreover, it does not require the user to specify the number of clusters in advance, making it an excellent choice for exploratory data analysis. However, it is important to note that agglomerative clustering can be computationally intensive, especially with large datasets, as the distance matrix must be recalculated after each merge.Another significant aspect of agglomerative clustering is the linkage criteria used to determine the distance between clusters. Common linkage methods include single linkage, complete linkage, average linkage, and Ward's method. Each of these methods has different implications for how clusters are formed and can lead to different clustering results. For instance, single linkage tends to create long, chain-like clusters, while complete linkage tends to produce more compact clusters. Understanding these differences is crucial for selecting the appropriate method for a given dataset.In practical applications, agglomerative clustering is widely used in various domains, including marketing, biology, and social sciences. For example, in marketing, businesses can use agglomerative clustering to segment customers based on purchasing behavior, allowing them to tailor their marketing strategies effectively. In biology, researchers might apply this method to group similar species based on genetic data, aiding in the understanding of evolutionary relationships.Despite its advantages, agglomerative clustering also has limitations. One major drawback is its sensitivity to noise and outliers, which can significantly affect the clustering results. Additionally, because it is a hierarchical method, once a merge is made, it cannot be undone, which can lead to suboptimal solutions if early decisions were not ideal. Therefore, it is essential to preprocess the data carefully and consider using techniques such as outlier detection before applying agglomerative clustering.In conclusion, agglomerative clustering is a powerful and versatile technique for grouping similar data points in various fields. Its bottom-up approach, flexibility with different data types, and applicability to exploratory analysis make it a valuable tool for data scientists. However, users must be aware of its limitations and take necessary precautions to ensure meaningful and accurate clustering results. As data continues to grow in complexity and volume, methods like agglomerative clustering will remain crucial for uncovering insights and patterns within datasets.

在数据科学和机器学习领域，聚类是一种用于将相似数据点分组的基本技术。其中一种最流行的聚类方法被称为凝聚聚类。这种方法在我们需要理解数据集的固有结构而没有先验知识关于聚类数量的情况下特别有用。凝聚这个术语指的是自下而上的方法，其中每个数据点最初作为自己的聚类，基于相似性将成对的聚类合并在一起，直到剩下一个聚类或达到指定的聚类数量。凝聚聚类的过程始于计算距离矩阵，该矩阵量化了所有数据点对之间的相似性。可以使用各种距离度量，例如欧几里得距离、曼哈顿距离或余弦相似度，具体取决于数据的性质和分析的特定要求。在建立这个矩阵之后，算法识别出两个最接近的聚类并将它们合并为一个新聚类。这个过程反复进行，导致越来越大的聚类，直到达到所需的聚类数量。凝聚聚类的一个优点是其灵活性。它可以应用于各种类型的数据，包括数值型、分类和混合数据类型。此外，它不要求用户提前指定聚类数量，这使其成为探索性数据分析的绝佳选择。然而，需要注意的是，凝聚聚类可能在处理大型数据集时计算密集，因为在每次合并后必须重新计算距离矩阵。凝聚聚类的另一个重要方面是用于确定聚类之间距离的链接标准。常见的链接方法包括单链接、完全链接、平均链接和Ward方法。每种方法对聚类的形成有不同的影响，并可能导致不同的聚类结果。例如，单链接倾向于创建长链状聚类，而完全链接则倾向于产生更紧凑的聚类。理解这些差异对选择适合给定数据集的方法至关重要。在实际应用中，凝聚聚类在市场营销、生物学和社会科学等各个领域得到了广泛应用。例如，在市场营销中，企业可以利用凝聚聚类根据购买行为对客户进行细分，从而有效地调整其营销策略。在生物学中，研究人员可能会应用这种方法根据基因数据对相似物种进行分组，从而帮助理解进化关系。尽管有其优点，凝聚聚类也有其局限性。一个主要缺点是对噪声和离群值的敏感性，这可能会显著影响聚类结果。此外，由于它是一种层次方法，一旦做出合并，就无法撤销，这可能会导致早期决策不理想的情况。因此，仔细预处理数据并考虑在应用凝聚聚类之前使用诸如离群值检测等技术是至关重要的。总之，凝聚聚类是一种强大而多功能的技术，用于在各个领域中对相似数据点进行分组。其自下而上的方法、对不同数据类型的灵活性以及对探索性分析的适用性使其成为数据科学家的宝贵工具。然而，用户必须意识到其局限性，并采取必要的预防措施以确保有意义和准确的聚类结果。随着数据复杂性和数量的不断增长，像凝聚聚类这样的方法将继续在揭示数据集中的洞察和模式中发挥关键作用。