Cluster Analysis in Data Mining

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7/10 stars
based on  4 reviews
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Cost FREE
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Course Details

Cost

FREE

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  • TBA

Course Provider

Coursera online courses
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with yo...
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with your coursework. Coursera also partners with the US State Department to create “learning hubs” around the world. Students can get internet access, take courses, and participate in weekly in-person study groups to make learning even more collaborative. Begin your journey into the mysteries of the human brain by taking courses in neuroscience. Learn how to navigate the data infrastructures that multinational corporations use when you discover the world of data analysis. Follow one of Coursera’s “Skill Tracks”. Or try any one of its more than 560 available courses to help you achieve your academic and professional goals.

Provider Subject Specialization
Humanities
Sciences & Technology
4715 reviews

Course Description

Learn how to take scattered data and organize it into groups for use in many applications, such as market analysis and biomedical data analysis, or as a pre-processing step for many data mining tasks.
Reviews 7/10 stars
4 Reviews for Cluster Analysis in Data Mining

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Greg Hamel profile image
Greg Hamel profile image
4/10 starsCompleted
  • 116 reviews
  • 107 completed
4 years, 2 months ago
Cluster Analysis in Data Mining is third course in Coursera's new data mining specialization offered by the University of Illinois Urbana-Champaign. The course is a 4-week overview of data clustering: unsupervised learning methods that attempt to group data into clusters of related or similar observations. The course covers two most common clustering methods--K means and hierarchical clustering--as well as more than a dozen other clustering algorithms. Grading is based on 4 weekly quizzes with 3 attempts each. Cluster Analysis is taught by Professor Jiawei Han who was the instructor for the first course in the data mining specialization: Pattern Discovery in Data Mining. The quality of the slides, instruction and organization of materials in this course is slightly better than the pattern discovery course, but that isn't saying much: it is still below Coursera's usual high standards. The course rushes from one topic to another w... Cluster Analysis in Data Mining is third course in Coursera's new data mining specialization offered by the University of Illinois Urbana-Champaign. The course is a 4-week overview of data clustering: unsupervised learning methods that attempt to group data into clusters of related or similar observations. The course covers two most common clustering methods--K means and hierarchical clustering--as well as more than a dozen other clustering algorithms. Grading is based on 4 weekly quizzes with 3 attempts each. Cluster Analysis is taught by Professor Jiawei Han who was the instructor for the first course in the data mining specialization: Pattern Discovery in Data Mining. The quality of the slides, instruction and organization of materials in this course is slightly better than the pattern discovery course, but that isn't saying much: it is still below Coursera's usual high standards. The course rushes from one topic to another with instruction that is mediocre at best downright confusing at its worst. That's not to say you can't learn anything from this course, but the instruction is often more of a hindrance than a help. There are occasional in-lecture quizzes, but the graded quizzes largely fail to foster any understanding of the material. An optional programming assignment was added half way through the course; in a course about data mining, programming assignments should be front and center, not added as an afterthought to quell an outcry from students. Cluster Analysis in Data Mining is another disappointing entry in Coursera's data mining specialization. Although the course covers many different clustering methods, poor instruction makes it hard to gain a good understanding of the material unless you are extremely attentive or watch the videos several times. I give Cluster Analysis in Data Mining 2 out of 5 stars: Poor.
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Kristina Šekrst profile image
Kristina Šekrst profile image
8/10 starsCompleted
  • 102 reviews
  • 102 completed
3 years, 10 months ago
I liked the way I was able to learn more about the newest trends in clustering algorithms, but there was too much theory, and too little practice. However, it was a fun experience, but I hope in the second iteration that the ratio of the programming assignments and the theoretical descriptions of various algorithms and papers will be equal.
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Hamideh Iraj profile image
Hamideh Iraj profile image
6/10 starsCompleted
  • 70 reviews
  • 60 completed
4 years, 1 month ago
This course is my first MOOC in machine learning that goes beyond introductory level and explores clustering algorithms in 4 weeks. This is generally a low-quality course. The syllabus is interesting technically but spiritless due to the lack of context and use cases to give a meaning to algorithms. The lectures are deadly boring. The instructor obviously lacks teaching and communication skills and most of the time he is reading from the slides. This course helps you to know only the titles of available clustering algorithms . You will not learn any of them. It only warms you up to study the subjects later. I am taking this course just to get an idea about clustering algorithms and self-study in demand. It is worth spending about two hours a week for watching each week videos and doing the quizzes in one our or so. This is the first run of the course and it has many ways to improve.
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Richard Taylor profile image
Richard Taylor profile image
8/10 starsCompleted
  • 29 reviews
  • 28 completed
4 years, 1 month ago
This is a good course on Clustering algorithms. The instructor covers a lot of topics and provides good examples to understand the most important algorithms. I liked the detailed and numerous examples that were explained through the course. It is not a perfect course as real-life applications of clustering are missing and the programming assignment doesn't make any sense but overall I liked the content and how it was presented.
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