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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/cluster-analysis
课程评论：没有评论
课程名称：数据挖掘中的聚类分析 课程概述：本课程将引导您了解聚类分析的基本概念，并深入研究一系列典型的聚类方法、算法和应用。这包括分区方法（如k均值法）、层次方法（如BIRCH）和基于密度的方法（如DBSCAN和OPTICS）。此外，课程还将教授聚类验证及聚类质量评估的方法。最后，您将看到聚类分析在实际应用中的示例。 课程大纲： - 课程介绍：您将熟悉课程内容、同学和学习环境。此次介绍还将帮助您掌握课程所需的技术技能。 - 模块1：细节待定 - 第2周：细节待定 - 第3周：细节待定 - 第4周：细节待定 - 课程总结：在课程总结中，欢迎您分享对此次课程体验的任何感想。 本课程适合希望提高数据分析能力和学习聚类技术的学员。
Name:Course Orientation
Description:You will become familiar with the course, your classmates, and our learning environment. The orientation will also help you obtain the technical skills required for the course.
Name:Module 1
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Name:Week 2
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Name:Week 3
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Name:Week 4
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Name:Course Conclusion
Description:In the course conclusion, feel free to share any thoughts you have on this course experience.
Discover the basic concepts of cluster analysis, and then study a set of typical clustering methodologies, algorithms, and applications. This includes partitioning methods such as k-means, hierarchical methods such as BIRCH, and density-based methods such as DBSCAN/OPTICS. Moreover, learn methods for clustering validation and evaluation of clustering quality. Finally, see examples of cluster analysis in applications.
