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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About this Course
최근 조회 16,647회
귀하가 습득할 기술
Cluster AnalysisData Clustering AlgorithmsK-Means ClusteringHierarchical Clustering
강의 계획 - 이 강좌에서 배울 내용
주
1완료하는 데 1시간 필요
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.
1 video (Total 7 min), 3 readings, 1 quiz
1개의 동영상
3개의 읽기 자료
Syllabus10m
About the Discussion Forums10m
Social Media10m
1개 연습문제
Orientation Quiz10m
완료하는 데 2시간 필요
Module 1
13 videos (Total 65 min), 2 readings, 2 quizzes
13개의 동영상
1.2. Applications of Cluster Analysis2m
1.3 Requirements and Challenges5m
1.4 A Multi-Dimensional Categorization2m
1.5 An Overview of Typical Clustering Methodologies6m
1.6 An Overview of Clustering Different Types of Data6m
1.7 An Overview of User Insights and Clustering3m
2.1 Basic Concepts: Measuring Similarity between Objects3m
2.2 Distance on Numeric Data Minkowski Distance7m
2.3 Proximity Measure for Symetric vs Asymmetric Binary Variables4m
2.4 Distance between Categorical Attributes Ordinal Attributes and Mixed Types4m
2.5 Proximity Measure between Two Vectors Cosine Similarity2m
2.6 Correlation Measures between Two variables Covariance and Correlation Coefficient13m
2개의 읽기 자료
Lesson 1 Overview10m
Lesson 2 Overview10m
2개 연습문제
Lesson 1 Quiz8m
Lesson 2 Quiz12m
주
2완료하는 데 5시간 필요
Week 2
15 videos (Total 78 min), 3 readings, 2 quizzes
15개의 동영상
3.2 K-Means Clustering Method9m
3.3 Initialization of K-Means Clustering4m
3.4 The K-Medoids Clustering Method6m
3.5 The K-Medians and K-Modes Clustering Methods6m
3.6 Kernel K-Means Clustering8m
4.1 Hierarchical Clustering Methods1m
4.2 Agglomerative Clustering Algorithms8m
4.3 Divisive Clustering Algorithms3m
4.4 Extensions to Hierarchical Clustering3m
4.5 BIRCH: A Micro-Clustering-Based Approach7m
ClusterEnG Overview5m
ClusterEnG: K-Means and K-Medoids3m
ClusterEnG Application: AGNES4m
ClusterEnG Application: DBSCAN2m
3개의 읽기 자료
Lesson 3 Overview10m
Lesson 4 Part 1 Overview10m
ClusterEnG Introduction10m
1개 연습문제
Lesson 3 Quiz10m
주
3완료하는 데 1시간 필요
Week 3
9 videos (Total 53 min), 2 readings, 2 quizzes
9개의 동영상
4.7 CHAMELEON: Graph Partitioning on the KNN Graph of the Data8m
4.8 Probabilistic Hierarchical Clustering7m
5.1 Density-Based and Grid-Based Clustering Methods1m
5.2 DBSCAN: A Density-Based Clustering Algorithm8m
5.3 OPTICS: Ordering Points To Identify Clustering Structure9m
5.4 Grid-Based Clustering Methods3m
5.5 STING: A Statistical Information Grid Approach3m
5.6 CLIQUE: Grid-Based Subspace Clustering7m
2개의 읽기 자료
Lesson 4 Part 2 Overview10m
Lesson 5 Overview10m
2개 연습문제
Lesson 4 Quiz8m
Lesson 5 Quiz8m
주
4완료하는 데 4시간 필요
Week 4
10 videos (Total 57 min), 1 reading, 2 quizzes
10개의 동영상
6.2 Clustering Evaluation Measuring Clustering Quality2m
6.3 Constraint-Based Clustering4m
6.4 External Measures 1: Matching-Based Measures10m
6.5 External Measure 2: Entropy-Based Measures7m
6.6 External Measure 3: Pairwise Measures6m
6.7 Internal Measures for Clustering Validation7m
6.8 Relative Measures5m
6.9 Cluster Stability6m
6.10 Clustering Tendency5m
1개의 읽기 자료
Lesson 6 Overview10m
1개 연습문제
Lesson 6 Quiz8m
완료하는 데 20분 필요
Course Conclusion
In the course conclusion, feel free to share any thoughts you have on this course experience.
4.4
40개의 리뷰33%
이 강좌를 수료한 후 새로운 경력 시작하기
25%
이 강좌를 통해 확실한 경력상 이점 얻기
50%
급여 인상 또는 승진하기
Cluster Analysis in Data Mining의 최상위 리뷰
This was my favorite course in the whole specialization. Everything is explained very concisely and clearly making the subject matter very easy to understand.
A very good course, it gives me a general idea of how clustering algorithm work.
강사
Jiawei Han
Abel Bliss ProfessorDepartment of Computer Science
Start working towards your Master's degree
이 강좌은(는) 일리노이대학교 어버너-섐페인캠퍼스의 100% 온라인 Master in Computer Science 중 일부입니다.
전체 프로그램을 수료하면 귀하의 강좌가 학위 취득에 반영됩니다.
일리노이대학교 어버너-섐페인캠퍼스 정보
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.
데이터 마이닝 전문 분야 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.
Courses 2 - 5 of this Specialization form the lecture component of courses in the online Master of Computer Science Degree in Data Science. You can apply to the degree program either before or after you begin the Specialization.
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