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: 359
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
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51–55 из 55 отзывов о курсе Анализ кластеров в процессе анализа данных
автор: aditya p
•15 февр. 2017 г.
good course!
автор: prasanna k p
•22 нояб. 2019 г.
it will be very helpful for understanding if any examples given with dummy data for cluster evaluation
автор: Aden G
•15 окт. 2016 г.
I am concerned about the last assignment of this course. And I cannot get any help from here.
автор: Alexandre M B
•11 нояб. 2017 г.
My analysis is that the assessments do not match the depth of what is explained.
автор: Logan V
•27 июня 2020 г.
needs examples
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