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Вернуться к Анализ кластеров в процессе анализа данных

Отзывы учащихся о курсе Анализ кластеров в процессе анализа данных от партнера Иллинойсский университет в Урбане-Шампейне

Оценки: 380
Рецензии: 60

О курсе

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....

Лучшие рецензии

17 дек. 2018 г.

This was my favorite course in the whole specialization. Everything is explained very concisely and clearly making the subject matter very easy to understand.

6 нояб. 2019 г.

Good course for understanding the Cluster Analysis & Algorithms, instructor is very experienced and well explained, thanks

Фильтр по:

26–50 из 60 отзывов о курсе Анализ кластеров в процессе анализа данных

автор: Dr. P N

14 окт. 2020 г.

A wonderful learning experience !

автор: Pavan G

2 окт. 2017 г.

Explained with nice examples

автор: Leela P

16 янв. 2017 г.

Very useful and well taught


1 мая 2017 г.

Clustering demytified

автор: Ankit

12 февр. 2020 г.

Fantastic course

автор: Christopher D

8 нояб. 2016 г.

Great course!

автор: VIDUSHI M

17 мар. 2019 г.


автор: KRUPAL J K

9 апр. 2019 г.


автор: Oren

7 июня 2017 г.

Very good

автор: Hernán C V

1 июля 2017 г.


автор: vaseem a

8 апр. 2019 г.


автор: Alan J R

20 февр. 2020 г.


автор: Valerie P

11 июля 2017 г.


автор: geoffrey a

2 сент. 2017 г.

Good, thorough coverage -- for a 4-week course -- of how to cluster. I liked the evaluation of clustering topic especially. Very few other instructors seem to discuss the vitally important evaluation of clustering results in any depth when they teach clustering. Dr. Han explained a comprehensive framework for understanding the effectiveness of any clustering system. I had never seen some of this material before, even though clustering was a topic appearing in a couple of other data science or machine learning courses that I have taken in the past. Ideally I would even wish to see this course extended to 6 or 8 weeks, so that case studies on difficult real datasets can be clustered. For example I had a terribly difficult ordeal last year before I took this course, trying to cluster the dataset of the BOSCH competition. It has about 90% missing data in every row, and there are 2 million rows in total, and about 4500 columns! Kaggle's BOSCH is a SUPER tough dataset to work with! I hope to come back to try the BOSCH dataset again using my new knowledge of clustering some time soon. The reason I chose to run unsupervised clustering on this BOSCH dataset, which is ostensibly intended for supervised learning, is to eliminate significant amounts of the missing data from being exposed to multiple individual supervised learning models by prior clever grouping of examples. I am still postulating to the current day that clustering and creating another unique supervised learning model for each cluster is the most important step to eliminating missing data in this particular problem.

автор: David M L H

12 июня 2020 г.

Enjoyed the course. Though there is no programming content, the assignments require such. So, participants should have some prerequisite skills in either R, Phyton or other statistical software to perform. What I like is that the contents cover the "maths" of cluster analysis, though not very deep.

автор: Cassius d O P

17 апр. 2021 г.

It was definitely an instructive course. 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.

автор: GANG L

26 янв. 2018 г.

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.

автор: Devender B

10 мар. 2019 г.

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.

автор: Umesh G

28 апр. 2019 г.

Its Good but explanations can done much better, rest all good in terms of study material, quiz ,and programming assignment.

автор: Haf M

22 июля 2021 г.

The course is good. learned alot but videos are boring and hard to understand due to more and more text on slides

автор: Alexander S

16 дек. 2019 г.

Good course. Some of the slides have value errors. Explanations for the programming assignments could be better.

автор: Anubhav B

7 нояб. 2016 г.

The course is very insightful and very helpful for the data mining studies at university courses.

автор: Ridowati G

24 янв. 2021 г.

The material is too general, does not provide examples. So it's difficult when doing the exam.

автор: PREETAM R

28 июля 2020 г.

Covers great deal of topics and various aspects of clustering

автор: shane

7 сент. 2017 г.

Very detailed introduction of Clustering techniques.