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.
von
Spezialisierung Data-Mining University of Illinois at Urbana-ChampaignÜber diesen Kurs
30,793 kürzliche Aufrufe
Karriereergebnisse der Lernenden
25%
nahm einen neuen Beruf nach Abschluss dieser Kurse auf
17%
ziehen Sie für Ihren Beruf greifbaren Nutzen aus diesem Kurs
33%
erhalten Sie eine Gehaltserhöhung oder Beförderung
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Kurs 5 von 6 in
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Ca. 14 Stunden zum Abschließen
Englisch
Untertitel: Englisch, Koreanisch
Kompetenzen, die Sie erwerben
Cluster AnalysisData Clustering AlgorithmsK-Means ClusteringHierarchical Clustering
Karriereergebnisse der Lernenden
25%
nahm einen neuen Beruf nach Abschluss dieser Kurse auf
17%
ziehen Sie für Ihren Beruf greifbaren Nutzen aus diesem Kurs
33%
erhalten Sie eine Gehaltserhöhung oder Beförderung
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Kurs 5 von 6 in
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Ca. 14 Stunden zum Abschließen
Englisch
Untertitel: Englisch, Koreanisch
von
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.
Beginnen Sie damit, auf Ihren Master-Abschluss hinzuarbeiten.
Dieses Kurs ist Teil des reinen Onlineabschlusses Master in Computer Science von University of Illinois at Urbana-Champaign.
Wenn Sie in das komplette Programm aufgenommen werden, werden Ihre Kurse auf Ihren Abschluss angerechnet.
Lehrplan - Was Sie in diesem Kurs lernen werden
1 Stunde zum Abschließen
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 Stunde zum Abschließen
1 Video (Gesamt 7 min), 3 Lektüren, 1 Quiz
1 Video
3 Lektüren
Syllabus10m
About the Discussion Forums10m
Social Media10m
1 praktische Übung
Orientation Quiz10m
2 Stunden zum Abschließen
Module 1
2 Stunden zum Abschließen
13 Videos (Gesamt 65 min), 2 Lektüren, 2 Quiz
13 Videos
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
2 Lektüren
Lesson 1 Overview10m
Lesson 2 Overview10m
2 praktische Übungen
Lesson 1 Quiz8m
Lesson 2 Quiz12m
5 Stunden zum Abschließen
Week 2
5 Stunden zum Abschließen
15 Videos (Gesamt 78 min), 3 Lektüren, 2 Quiz
15 Videos
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
3 Lektüren
Lesson 3 Overview10m
Lesson 4 Part 1 Overview10m
ClusterEnG Introduction10m
1 praktische Übung
Lesson 3 Quiz10m
1 Stunde zum Abschließen
Week 3
1 Stunde zum Abschließen
9 Videos (Gesamt 53 min), 2 Lektüren, 2 Quiz
9 Videos
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
2 Lektüren
Lesson 4 Part 2 Overview10m
Lesson 5 Overview10m
2 praktische Übungen
Lesson 4 Quiz8m
Lesson 5 Quiz8m
4 Stunden zum Abschließen
Week 4
4 Stunden zum Abschließen
10 Videos (Gesamt 57 min), 1 Lektüre, 2 Quiz
10 Videos
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
1 Lektüre
Lesson 6 Overview10m
1 praktische Übung
Lesson 6 Quiz8m
20 Minuten zum Abschließen
Course Conclusion
In the course conclusion, feel free to share any thoughts you have on this course experience.
20 Minuten zum Abschließen
Bewertungen
Top-Bewertungen von CLUSTER ANALYSIS IN DATA MINING
von GL25. Jan. 2018
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.
von DB9. März 2019
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.
von ES17. Dez. 2018
This was my favorite course in the whole specialization. Everything is explained very concisely and clearly making the subject matter very easy to understand.
von UG27. Apr. 2019
Its Good but explanations can done much better, rest all good in terms of study material, quiz ,and programming assignment.
Über den Spezialisierung Data-Mining
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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The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
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