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.
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이 강좌에 대하여
귀하가 습득할 기술
- Cluster Analysis
- Data Clustering Algorithms
- K-Means Clustering
- Hierarchical Clustering
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일리노이대학교 어버너-섐페인캠퍼스
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석사 학위 취득 시작
강의 계획표 - 이 강좌에서 배울 내용
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.
검토
- 5 stars66.58%
- 4 stars23.29%
- 3 stars5.56%
- 2 stars2.02%
- 1 star2.53%
CLUSTER ANALYSIS IN DATA MINING의 최상위 리뷰
Good course for understanding the Cluster Analysis & Algorithms, instructor is very experienced and well explained, thanks
The material is too general, does not provide examples. So it's difficult when doing the exam.
Very intense and required complex thinking and programming skill
Covers great deal of topics and various aspects of clustering
데이터 마이닝 특화 과정 정보
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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