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.

课程信息
学生职业成果
25%
17%
33%
您将获得的技能
学生职业成果
25%
17%
33%
提供方

伊利诺伊大学香槟分校
The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.
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教学大纲 - 您将从这门课程中学到什么
Course Orientation
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.
Module 1
Week 2
Week 3
Week 4
Course Conclusion
In the course conclusion, feel free to share any thoughts you have on this course experience.
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来自数据挖掘中的聚类分析的热门评论
This is a very good course covering all area of clustering. The only thing I feel a little struggle is some algorithm explained too brief, I prefer some detail step by step examples.
This was my favorite course in the whole specialization. Everything is explained very concisely and clearly making the subject matter very easy to understand.
Useful theory. It will be challenging for non-math students. and also lecturer's native language influence iis going to be challening as well to follow along.
Its Good but explanations can done much better, rest all good in terms of study material, quiz ,and programming assignment.
关于 数据挖掘 专项课程
The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp.

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