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.
データマイニング の専門講座イリノイ大学アーバナ・シャンペーン校（University of Illinois at Urbana-Champaign）このコースについて
35,556 最近の表示
受講生の就業成果
25%
コース終了後に新しいキャリアをスタートした
17%
コースが具体的なキャリアアップにつながった
33%
昇給や昇進につながった
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における6の5コース
柔軟性のある期限
スケジュールに従って期限をリセットします。
約14時間で修了
英語
字幕：英語, 韓国語
習得するスキル
Cluster AnalysisData Clustering AlgorithmsK-Means ClusteringHierarchical Clustering
受講生の就業成果
25%
コース終了後に新しいキャリアをスタートした
17%
コースが具体的なキャリアアップにつながった
33%
昇給や昇進につながった
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における6の5コース
柔軟性のある期限
スケジュールに従って期限をリセットします。
約14時間で修了
英語
字幕：英語, 韓国語
提供：
イリノイ大学アーバナ・シャンペーン校（University of Illinois at Urbana-Champaign）
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.
修士号の取得を目指しましょう
この コース は イリノイ大学アーバナ・シャンペーン校（University of Illinois at Urbana-Champaign） の100%オンラインの Master in Computer Science の一部です。
プログラムのすべてで認定されれば、それらのコースが学位学習に加算されます。
シラバス - 本コースの学習内容
1時間で修了
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時間で修了
1件のビデオ （合計7分）, 3 readings, 1 quiz
1件のビデオ
3件の学習用教材
Syllabus10 分
About the Discussion Forums10 分
Social Media10 分
1の練習問題
Orientation Quiz10 分
2時間で修了
Module 1
2時間で修了
13件のビデオ （合計65分）, 2 readings, 2 quizzes
13件のビデオ
1.2. Applications of Cluster Analysis2 分
1.3 Requirements and Challenges5 分
1.4 A Multi-Dimensional Categorization2 分
1.5 An Overview of Typical Clustering Methodologies6 分
1.6 An Overview of Clustering Different Types of Data6 分
1.7 An Overview of User Insights and Clustering3 分
2.1 Basic Concepts: Measuring Similarity between Objects3 分
2.2 Distance on Numeric Data Minkowski Distance7 分
2.3 Proximity Measure for Symetric vs Asymmetric Binary Variables4 分
2.4 Distance between Categorical Attributes Ordinal Attributes and Mixed Types4 分
2.5 Proximity Measure between Two Vectors Cosine Similarity2 分
2.6 Correlation Measures between Two variables Covariance and Correlation Coefficient13 分
2件の学習用教材
Lesson 1 Overview10 分
Lesson 2 Overview10 分
2の練習問題
Lesson 1 Quiz8 分
Lesson 2 Quiz12 分
5時間で修了
Week 2
5時間で修了
15件のビデオ （合計78分）, 3 readings, 2 quizzes
15件のビデオ
3.2 K-Means Clustering Method9 分
3.3 Initialization of K-Means Clustering4 分
3.4 The K-Medoids Clustering Method6 分
3.5 The K-Medians and K-Modes Clustering Methods6 分
3.6 Kernel K-Means Clustering8 分
4.1 Hierarchical Clustering Methods1 分
4.2 Agglomerative Clustering Algorithms8 分
4.3 Divisive Clustering Algorithms3 分
4.4 Extensions to Hierarchical Clustering3 分
4.5 BIRCH: A Micro-Clustering-Based Approach7 分
ClusterEnG Overview5 分
ClusterEnG: K-Means and K-Medoids3 分
ClusterEnG Application: AGNES4 分
ClusterEnG Application: DBSCAN2 分
3件の学習用教材
Lesson 3 Overview10 分
Lesson 4 Part 1 Overview10 分
ClusterEnG Introduction10 分
1の練習問題
Lesson 3 Quiz10 分
1時間で修了
Week 3
1時間で修了
9件のビデオ （合計53分）, 2 readings, 2 quizzes
9件のビデオ
4.7 CHAMELEON: Graph Partitioning on the KNN Graph of the Data8 分
4.8 Probabilistic Hierarchical Clustering7 分
5.1 Density-Based and Grid-Based Clustering Methods1 分
5.2 DBSCAN: A Density-Based Clustering Algorithm8 分
5.3 OPTICS: Ordering Points To Identify Clustering Structure9 分
5.4 Grid-Based Clustering Methods3 分
5.5 STING: A Statistical Information Grid Approach3 分
5.6 CLIQUE: Grid-Based Subspace Clustering7 分
2件の学習用教材
Lesson 4 Part 2 Overview10 分
Lesson 5 Overview10 分
2の練習問題
Lesson 4 Quiz8 分
Lesson 5 Quiz8 分
4時間で修了
Week 4
4時間で修了
10件のビデオ （合計57分）, 1 reading, 2 quizzes
10件のビデオ
6.2 Clustering Evaluation Measuring Clustering Quality2 分
6.3 Constraint-Based Clustering4 分
6.4 External Measures 1: Matching-Based Measures10 分
6.5 External Measure 2: Entropy-Based Measures7 分
6.6 External Measure 3: Pairwise Measures6 分
6.7 Internal Measures for Clustering Validation7 分
6.8 Relative Measures5 分
6.9 Cluster Stability6 分
6.10 Clustering Tendency5 分
1件の学習用教材
Lesson 6 Overview10 分
1の練習問題
Lesson 6 Quiz8 分
20分で修了
Course Conclusion
In the course conclusion, feel free to share any thoughts you have on this course experience.
20分で修了
レビュー
CLUSTER ANALYSIS IN DATA MINING からの人気レビュー
GLにより2018年1月25日
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.
DBにより2019年3月9日
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.
ESにより2018年12月17日
This was my favorite course in the whole specialization. Everything is explained very concisely and clearly making the subject matter very easy to understand.
UGにより2019年4月27日
Its Good but explanations can done much better, rest all good in terms of study material, quiz ,and programming assignment.
データマイニング 専門講座について
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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