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Jiawei Han
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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What's inside
Syllabus
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.
Read more
Syllabus
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.
Read more
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.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores basic concepts of cluster analysis, which is fundamental in data mining
Examines typical clustering methodologies, algorithms, and applications, providing practical knowledge
Taught by Jiawei Han, an author of renowned textbooks on data mining and knowledge discovery
Introduces partitioning methods, hierarchical methods, and density-based methods, covering a range of clustering techniques
Includes methods for clustering validation and evaluation of clustering quality, ensuring reliable results
Provides examples of cluster analysis in applications, showcasing real-world problem-solving
Save this course
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Reviews summary
Intensive cluster analysis course
Learners say this course offers a detailed overview of cluster analysis. Covering more than a dozen algorithms, this course is paced over four weeks. With weekly quizzes, learners will delve into topics like K-means clustering and hierarchical clustering. While it lacks depth and learners may wish for more practical examples, it is rich in content and suitable for both beginners and more advanced learners.
Covers many algorithms and clustering methods.
"The course covers two most common clustering methods--K means and hierarchical clustering--as well as more than a dozen other clustering algorithms."
Limited depth in specific calculations.
"Okay as an introduction to key concepts. Lack of depth into the specific calculations."
"The material is too general, does not provide examples. So it's difficult when doing the exam."
Limited practical examples and applications.
"The material is too general, does not provide examples. So it's difficult when doing the exam."
"I liked a lot the insights and discussion about different clustering methods and algorithms. The downside of this course is the scanty discussion about the practical implementation/usage of these algorithms."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Cluster Analysis in Data Mining with these
activities:
Review clustering algorithms
Show steps
Review the basic algorithms and concepts of clustering will help establish the foundation needed to be successful in this course.
Browse courses on
Clustering Algorithms
Show steps
-
Read over the slides and notes from the previous clustering course you took
-
Review the k-means algorithm, the BIRCH hierarchical clustering algorithm, and the DBSCAN/OPTICS density-based clustering algorithm
-
Work through some practice problems on clustering
Read 'Clustering Algorithms' by Jiawei Han and Micheline Kamber
Show steps
Reading this book will provide you with a comprehensive overview of clustering algorithms and how to use them to solve real-world problems.
View
Data Mining: Concepts and Techniques
on Amazon
Show steps
-
Read the book's introduction and first chapter
-
Read the chapters on partitioning methods, hierarchical methods, and density-based methods
-
Read the book's conclusion and summary
Clustering practice problems
Show steps
Working through practice problems will help solidify your understanding of clustering algorithms and concepts.
Browse courses on
Clustering Algorithms
Show steps
-
Find a set of practice problems on clustering algorithms
-
Work through the problems, making sure to understand the steps involved in solving each problem
-
Check your answers against the solutions provided
Five other activities
Expand to see all activities and additional details
Show all eight activities
Clustering study group
Show steps
Participating in a study group will allow you to discuss the clustering algorithms and concepts you are learning in this course with other students.
Browse courses on
Clustering Algorithms
Show steps
-
Find a study group to join
-
Meet with your study group regularly to discuss the course material
-
Work together on practice problems and projects
Clustering tutorials
Show steps
Watching clustering tutorials will help you learn about the different clustering algorithms and how to use them.
Browse courses on
Clustering Algorithms
Show steps
-
Find a set of clustering tutorials
-
Watch the tutorials, taking notes on the key concepts
-
Try out the clustering algorithms on your own data
Mentor other students
Show steps
Mentoring other students will help you solidify your understanding of clustering algorithms and concepts.
Browse courses on
Clustering Algorithms
Show steps
-
Find a student who is struggling with the course material
-
Meet with the student regularly to help them understand the material
-
Answer the student's questions and provide them with feedback on their work
Clustering project
Show steps
Completing a clustering project will allow you to apply the clustering algorithms and concepts you have learned in this course to a real-world problem.
Browse courses on
Clustering Algorithms
Show steps
-
Choose a dataset to cluster
-
Select a clustering algorithm to use
-
Apply the clustering algorithm to the dataset
-
Evaluate the results of the clustering
-
Write a report on your findings
Contribute to open-source clustering projects
Show steps
Contributing to open-source clustering projects will allow you to learn about the latest developments in clustering algorithms and how to use them to solve real-world problems.
Browse courses on
Clustering Algorithms
Show steps
-
Find an open-source clustering project to contribute to
-
Read the documentation for the project
-
Make a contribution to the project
Review clustering algorithms
Show steps
Review the basic algorithms and concepts of clustering will help establish the foundation needed to be successful in this course.
Browse courses on
Clustering Algorithms
Show steps
Show steps
Show steps
- Read over the slides and notes from the previous clustering course you took
- Review the k-means algorithm, the BIRCH hierarchical clustering algorithm, and the DBSCAN/OPTICS density-based clustering algorithm
- Work through some practice problems on clustering
Read 'Clustering Algorithms' by Jiawei Han and Micheline Kamber
Show steps
Reading this book will provide you with a comprehensive overview of clustering algorithms and how to use them to solve real-world problems.
View
Data Mining: Concepts and Techniques
on Amazon
Show steps
Show steps
Show steps
- Read the book's introduction and first chapter
- Read the chapters on partitioning methods, hierarchical methods, and density-based methods
- Read the book's conclusion and summary
Clustering practice problems
Show steps
Working through practice problems will help solidify your understanding of clustering algorithms and concepts.
Browse courses on
Clustering Algorithms
Show steps
Show steps
Show steps
- Find a set of practice problems on clustering algorithms
- Work through the problems, making sure to understand the steps involved in solving each problem
- Check your answers against the solutions provided
Five other activities
Expand to see all activities and additional details
Show all eight activities
Clustering study group
Show steps
Participating in a study group will allow you to discuss the clustering algorithms and concepts you are learning in this course with other students.
Browse courses on
Clustering Algorithms
Show steps
Show steps
Show steps
- Find a study group to join
- Meet with your study group regularly to discuss the course material
- Work together on practice problems and projects
Clustering tutorials
Show steps
Watching clustering tutorials will help you learn about the different clustering algorithms and how to use them.
Browse courses on
Clustering Algorithms
Show steps
Show steps
Show steps
- Find a set of clustering tutorials
- Watch the tutorials, taking notes on the key concepts
- Try out the clustering algorithms on your own data
Mentor other students
Show steps
Mentoring other students will help you solidify your understanding of clustering algorithms and concepts.
Browse courses on
Clustering Algorithms
Show steps
Show steps
Show steps
- Find a student who is struggling with the course material
- Meet with the student regularly to help them understand the material
- Answer the student's questions and provide them with feedback on their work
Clustering project
Show steps
Completing a clustering project will allow you to apply the clustering algorithms and concepts you have learned in this course to a real-world problem.
Browse courses on
Clustering Algorithms
Show steps
Show steps
Show steps
- Choose a dataset to cluster
- Select a clustering algorithm to use
- Apply the clustering algorithm to the dataset
- Evaluate the results of the clustering
- Write a report on your findings
Contribute to open-source clustering projects
Show steps
Contributing to open-source clustering projects will allow you to learn about the latest developments in clustering algorithms and how to use them to solve real-world problems.
Browse courses on
Clustering Algorithms
Show steps
Show steps
Show steps
- Find an open-source clustering project to contribute to
- Read the documentation for the project
- Make a contribution to the project
Career center
Learners who complete Cluster Analysis in Data Mining will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist uses mathematical and statistical techniques to extract insights from data and develop predictive models. This course in Cluster Analysis in Data Mining aligns well with the responsibilities of a Data Scientist, as it deepens their understanding of clustering methodologies and helps them develop the skills to identify patterns and make predictions from complex datasets. The course's coverage of methods for clustering validation and evaluation of clustering quality is especially valuable for Data Scientists seeking to ensure the reliability and accuracy of their models.
Data Analyst
Data Analyst
A Data Analyst gathers, interprets, and presents data to provide insights and recommendations to organizations. This course, Cluster Analysis in Data Mining, provides a solid foundation for success in this role by introducing the fundamental concepts of cluster analysis and a range of clustering methodologies, algorithms, and applications. By understanding how to identify and group data into meaningful clusters, Data Analysts can effectively analyze large datasets, uncover patterns, and make data-driven decisions that support business objectives.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models to solve real-world problems. This course, Cluster Analysis in Data Mining, provides a strong foundation for this role by introducing the concepts and algorithms used in clustering, a fundamental technique in machine learning. By gaining expertise in cluster analysis, Machine Learning Engineers can enhance their ability to preprocess data, identify patterns, and develop more accurate and efficient machine learning models.
Statistician
Statistician
A Statistician collects, analyzes, interprets, and presents data to provide insights and make predictions. This course, Cluster Analysis in Data Mining, complements the skills of a Statistician by providing a comprehensive understanding of cluster analysis techniques and their applications in various fields. By mastering the concepts and methods covered in this course, Statisticians can enhance their ability to identify patterns, segment data, and make data-driven decisions.
Data Mining Engineer
Data Mining Engineer
A Data Mining Engineer designs and develops software tools and algorithms to extract knowledge from large datasets. This course in Cluster Analysis in Data Mining provides a strong foundation for success in this role by introducing the fundamental concepts and techniques of cluster analysis. By mastering the algorithms and methods covered in this course, Data Mining Engineers can develop more effective data mining tools and solutions that support decision-making and knowledge discovery.
Business Analyst
Business Analyst
A Business Analyst identifies and analyzes business needs and develops solutions to improve organizational performance. This course, Cluster Analysis in Data Mining, provides valuable insights for Business Analysts by introducing the techniques used to identify patterns and group data into meaningful clusters. By understanding how to apply cluster analysis, Business Analysts can gain a deeper understanding of customer segmentation, market analysis, and other business-critical areas.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze financial data and make investment decisions. This course, Cluster Analysis in Data Mining, provides valuable insights for Quantitative Analysts by introducing the techniques used to identify patterns and group data into meaningful clusters. By understanding how to apply cluster analysis, Quantitative Analysts can enhance their ability to segment markets, identify investment opportunities, and make data-driven decisions.
Database Administrator
Database Administrator
A Database Administrator manages and maintains an organization's database systems. This course, Cluster Analysis in Data Mining, may be useful for Database Administrators who seek to enhance their understanding of data analysis techniques and their applications in database management. By mastering the concepts and methods covered in this course, Database Administrators can gain a deeper understanding of how to optimize database systems for cluster analysis and other data-driven applications.
Actuary
Actuary
An Actuary uses mathematical and statistical techniques to assess risk and uncertainty. This course, Cluster Analysis in Data Mining, may be useful for Actuaries who seek to enhance their understanding of data analysis techniques and their applications in risk assessment. By mastering the concepts and methods covered in this course, Actuaries can gain a deeper understanding of how to identify patterns, segment data, and make data-driven decisions.
Epidemiologist
Epidemiologist
An Epidemiologist investigates the causes and patterns of disease and injury in populations. This course, Cluster Analysis in Data Mining, may be useful for Epidemiologists who seek to enhance their understanding of data analysis techniques and their applications in epidemiology. By mastering the concepts and methods covered in this course, Epidemiologists can gain a deeper understanding of how to identify disease clusters, analyze risk factors, and make data-driven decisions.
Biostatistician
Biostatistician
A Biostatistician applies statistical methods to solve problems in biology and medicine. This course, Cluster Analysis in Data Mining, may be useful for Biostatisticians who seek to enhance their understanding of data analysis techniques and their applications in biostatistics. By mastering the concepts and methods covered in this course, Biostatisticians can gain a deeper understanding of how to identify patterns, segment data, and make data-driven decisions.
Market Researcher
Market Researcher
A Market Researcher conducts research and analysis to understand market trends and consumer behavior. This course, Cluster Analysis in Data Mining, may be useful for Market Researchers who seek to enhance their understanding of data analysis techniques and their applications in market research. By mastering the concepts and methods covered in this course, Market Researchers can gain a deeper understanding of how to identify customer segments, analyze market trends, and make data-driven decisions.
Research Analyst
Research Analyst
A Research Analyst conducts research and analysis to provide insights and recommendations to organizations. This course, Cluster Analysis in Data Mining, may be useful for Research Analysts who seek to enhance their understanding of data analysis techniques and their applications in various fields. By mastering the concepts and methods covered in this course, Research Analysts can gain a deeper understanding of how to identify patterns, segment data, and make data-driven decisions.
Data Architect
Data Architect
A Data Architect designs and manages the architecture of an organization's data systems. This course, Cluster Analysis in Data Mining, may be useful for Data Architects who seek to enhance their understanding of data analysis techniques and their applications in data architecture. By mastering the concepts and methods covered in this course, Data Architects can gain a deeper understanding of how to design and implement data systems that support cluster analysis and other data-driven applications.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. This course, Cluster Analysis in Data Mining, may be useful for Software Engineers who seek to enhance their understanding of data analysis techniques and their applications in software development. By mastering the concepts and methods covered in this course, Software Engineers can gain a deeper understanding of how to design and develop software systems that support cluster analysis and other data-driven applications.
For more career information including salaries, visit:
OpenCourser.com/course/jthbt3/cluster
Reading list
We've selected six books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Cluster Analysis in Data Mining.
Comprehensive guide to cluster analysis, providing a thorough overview of the field and its applications. It covers a wide range of topics, from basic concepts to advanced algorithms, and includes real-world examples and case studies.
Save
Provides a comprehensive overview of data mining concepts and techniques, including clustering. It useful reference for anyone interested in learning more about data mining and its applications.
Provides a practical introduction to machine learning for data mining, covering a wide range of topics, including clustering. It useful resource for anyone interested in learning more about machine learning and its applications.
Save
Provides a comprehensive overview of pattern recognition and machine learning, including clustering. It useful reference for anyone interested in learning more about these topics.
Provides a comprehensive overview of statistical learning, including clustering. It useful reference for anyone interested in learning more about these topics.
This paper provides a comprehensive overview of cluster analysis, including its history, methods, and applications. It useful reference for anyone interested in learning more about cluster analysis.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/jthbt3/cluster
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Via
Coursera
Institution
University of Illinois at Urbana-Champaign
Instructor
Jiawei Han
Language
English
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آذربایجان دیلی
This course belongs to a
collection
called
Data Mining . It's comprised of six courses:
- Data Visualization
- Text Mining and Analytics
- Cluster Analysis in Data Mining (viewing)
- Pattern Discovery in Data Mining
- Text Retrieval and Search Engines
- Data Mining Project
Good to know
Explores basic concepts of cluster analysis, which is fundamental in data mining
Examines typical clustering methodologies, algorithms, and applications, providing practical knowledge
Taught by Jiawei Han, an author of renowned textbooks on data mining and knowledge discovery
Introduces partitioning methods, hierarchical methods, and density-based methods, covering a range of clustering techniques
Includes methods for clustering validation and evaluation of clustering quality, ensuring reliable results
Provides examples of cluster analysis in applications, showcasing real-world problem-solving
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