clusters

简明释义

英[ˈklʌstəz]美[ˈklʌstərz]

n. [植]簇；丛（cluster 的复数形式）；[计]群集

英英释义

单词用法

同义词

反义词

例句

1.You can find the calculation on sheet 3, "Clusters".

您可以在表格3 “集群”中找到计算方法。

2.Globular clusters once ruled the Milky Way.

球状星团曾经统治着银河系。

3.The plant bears its flowers in clusters.

这种植物开花成簇。

4.These small particles agglomerate together to form larger clusters.

这些颗粒聚结形成较大的团。

5.It bears clusters of mauve flowers in early summer.

初夏时节，它开出了一簇簇淡紫色的花儿。

6.Cobe is designed to see just the biggest structures, but astronomers would like to see much smaller hot spots as well, the seeds of local objects like clusters and superclusters of galaxies.

Cobe的设计只是为了观测最大的结构，但天文学家们也希望看到更小的热点，即星系团和超星系团等局部天体的种子。

7.During the meeting, we discussed the clusters (团体) of issues affecting our project.

在会议期间，我们讨论了影响我们项目的clusters（团体）问题。

8.The scientist observed that the bacteria formed distinct clusters (簇) in the petri dish.

科学家观察到细菌在培养皿中形成了明显的clusters（簇）。

9.The clusters (群体) of stars in the night sky are breathtaking.

夜空中星星的clusters（群体）令人叹为观止。

10.In data analysis, we often look for clusters (聚类) of similar data points.

在数据分析中，我们经常寻找相似数据点的clusters（聚类）。

11.The app uses machine learning to identify clusters (集群) of user preferences.

该应用程序使用机器学习来识别用户偏好的clusters（集群）。

作文

In the vast realm of data science, one of the most intriguing concepts is that of clusters（簇）. Clustering refers to the process of grouping a set of objects in such a way that objects in the same group, or cluster（簇）, are more similar to each other than to those in other groups. This concept is widely used across various fields, including marketing, biology, and social sciences, to identify patterns and insights from large datasets.For instance, in marketing, businesses often use clustering techniques to segment their customer base. By analyzing purchasing behaviors and preferences, companies can create distinct clusters（簇） of customers who share similar characteristics. This allows marketers to tailor their strategies and personalize their offerings, leading to increased customer satisfaction and loyalty. Understanding these clusters（簇） can help organizations optimize their marketing efforts and allocate resources more efficiently.In the field of biology, clustering plays a crucial role in the classification of species. Scientists often use clustering algorithms to analyze genetic data and group organisms into clusters（簇） based on their genetic similarities. This approach not only aids in understanding evolutionary relationships but also helps in identifying new species and conserving biodiversity. The identification of these clusters（簇） can lead to significant discoveries and advancements in biological research.Moreover, in social sciences, researchers utilize clustering to study human behavior and societal trends. For example, sociologists might analyze survey data to identify clusters（簇） of individuals with similar opinions or lifestyles. This information can be invaluable for policymakers and community leaders, as it provides insights into the needs and preferences of different segments of the population. By addressing the concerns of these clusters（簇）, they can implement more effective programs and initiatives.The importance of clusters（簇） extends beyond just academic research; it has practical applications in everyday life. For instance, when we use online platforms like Netflix or Spotify, algorithms analyze our preferences and behaviors to recommend content that aligns with our tastes. This recommendation system is essentially based on the identification of clusters（簇） of users with similar interests, enhancing our experience and engagement with the platform.However, while clustering is a powerful tool, it is essential to recognize its limitations. The effectiveness of clustering algorithms can be influenced by various factors, including the choice of algorithm, the number of clusters（簇） to create, and the quality of the input data. Poorly defined clusters（簇） can lead to misleading conclusions and ineffective strategies. Therefore, it is crucial for practitioners to carefully consider these aspects when applying clustering techniques.In conclusion, the concept of clusters（簇） is fundamental in various domains, providing valuable insights and facilitating informed decision-making. Whether in marketing, biology, or social sciences, understanding and leveraging clusters（簇） can lead to significant advancements and improvements. As we continue to generate and analyze vast amounts of data, the ability to identify meaningful clusters（簇） will remain an essential skill for researchers and professionals alike. Ultimately, the study of clusters（簇） not only enhances our understanding of complex systems but also empowers us to make better choices in our personal and professional lives.

在数据科学的广阔领域中，最引人入胜的概念之一就是clusters（簇）。聚类是指将一组对象进行分组的过程，使得同一组中的对象或cluster（簇）彼此之间的相似性高于与其他组中的对象。这个概念在市场营销、生物学和社会科学等多个领域广泛应用，用于从大型数据集中识别模式和洞察。例如，在市场营销中，企业常常使用聚类技术对客户群进行细分。通过分析购买行为和偏好，公司可以创建不同的clusters（簇），这些客户分享相似的特征。这使得营销人员能够量身定制他们的策略并个性化他们的产品，进而提高客户满意度和忠诚度。理解这些clusters（簇）可以帮助组织优化他们的营销工作，更有效地分配资源。在生物学领域，聚类在物种分类中起着至关重要的作用。科学家们常常使用聚类算法来分析基因数据，并根据遗传相似性将生物体分组为clusters（簇）。这种方法不仅有助于理解进化关系，还帮助识别新物种和保护生物多样性。这些clusters（簇）的识别可以导致重大的发现和生物研究的进展。此外，在社会科学中，研究人员利用聚类来研究人类行为和社会趋势。例如，社会学家可能会分析调查数据，以识别具有相似观点或生活方式的个体clusters（簇）。这些信息对政策制定者和社区领导者来说是非常宝贵的，因为它提供了不同人群需求和偏好的洞察。通过解决这些clusters（簇）的关注点，他们可以实施更有效的项目和倡议。clusters（簇）的重要性不仅仅局限于学术研究；它在日常生活中也有实际应用。例如，当我们使用Netflix或Spotify等在线平台时，算法会分析我们的偏好和行为，以推荐符合我们口味的内容。这个推荐系统本质上是基于识别具有相似兴趣的用户clusters（簇），增强了我们与平台的体验和参与感。然而，虽然聚类是一种强大的工具，但必须认识到其局限性。聚类算法的有效性可能受到多种因素的影响，包括算法选择、创建的clusters（簇）数量以及输入数据的质量。定义不良的clusters（簇）可能导致误导性结论和无效策略。因此，实践者在应用聚类技术时必须仔细考虑这些方面。总之，clusters（簇）这一概念在各个领域都是基础，为有价值的洞察和明智的决策提供了支持。无论是在市场营销、生物学还是社会科学，理解和利用clusters（簇）都可以带来显著的进步和改善。随着我们继续生成和分析大量数据，识别有意义的clusters（簇）的能力将始终是研究人员和专业人士的重要技能。最终，对clusters（簇）的研究不仅增强了我们对复杂系统的理解，还使我们能够在个人和职业生活中做出更好的选择。