Zurück zu Cluster Analysis in Data Mining

4.5

Sterne

325 Bewertungen

•

50 Bewertungen

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....

Dec 18, 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.

Nov 07, 2019

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

Filtern nach:

von NACHO

•Apr 16, 2017

Redundant, poor explanations and a complete lack of examples about the general concepts and the foundations of this discipline. The interaction between the teacher and the slides is limited to a reading exercise that does not provide any add value at all. Very dissapointed and still wondering if this course is worth my attention -and extremely limited time- or not. Plenty of room for improvement.

von zshowing

•Dec 25, 2017

The instructor basically reads the slides line by line, with very few examples.

von Daniel B

•Feb 21, 2017

I have sat through 4 of the lessons and I am not very impressed. I fell that the topic is very interesting, but the professor does not do a very good job explaining the algorithms. It may be because I do no have the textbook, but overall a rather poor course. There need to be a little more explanation beyond the slides.

von ADARSHPANDEY

•Dec 24, 2017

Course is very good I learnt about a lot of things related to clustering. Actually it is a very good introductory course in clustering compared to the resources available online in general. Although few things that I think might help improve the course

i) Course only implements K-Means which is a very simple algorithm, instead of this or in addition to this implementation of few advanced algorithms like DBSCAN or CHAMELEON should be added.

ii) A no. of times prof only seems to be reading the slides which make things a little bit unclear i.e, the sentences used should be more common or explanatory rather than just reading the slides which the student itself can.

Apart from these things I truly enjoyed and learned many new things.

Thank you everyone involved in developing this course

von Bernd D

•Oct 27, 2017

Great course that provides a good overview of different clustering approaches and how to deploy them to various problems. I found the lecture material unclear or vague at times, so that for certain topics understanding heavily depends on one diving through the provided reading material (which I found very helpful). However, the topic of evaluation is very dense in the lectures and the provided book chapters do not provide relevant insights as well, making the programming assignment for this part quite challenging (at least if not already deeply familiar already with the concepts involved). Be ready to invest effort to make the most of this.

von Gary C

•Jul 24, 2017

For some reason this course felt like it was hurriedly put together. At times the lectures were great, but many times a topic would literally be covered for seconds that would somehow become an involved quiz question. Now I don't mind briefly covering topics, understanding that cluster analysis is a complex topic with many facets. However the quizzes should reflect the lectures. Overall the course felt more like speed dating, when it should be more about the fundamentals of dating.

von Steve S

•Jul 18, 2018

I feel like the programming assignments could've been more involved/tied to the clustering algorithms themselves, rather than just submitting a text file with results (e.g., maybe solve a practical problem with an algorithm of choice). Quizzes sometimes contained ambiguous and/or poorly-written questions/answers. Some of the later lectures simply featured equations on a powerpoint and did not involve any examples on how to use them.

von Martin L

•Dec 14, 2016

Just read the slide., The presentations add very little since the presenter is (stumbling) over just reading the text on the slides.

von Lei Z

•Dec 30, 2016

too theoretical without enough practical quiz and assignment

von barbara

•Aug 01, 2018

This course is a great resource to learn about the different clustering algorithms out there. I need to solve a clustering problem in my research and my knowledge about clustering ended at kmeans. The course teaches systematic ways to find out whether you should be clustering your data in the first place, what clustering algorithm should be best for your data, and how to evaluate the goodness of the algorithm and the used parameters. Many unknown unknowns have been illuminated to me by the course.

von Jose A E H

•Jul 12, 2017

This course along with the Reading material proposed will give you a big picture of how clustering algorithms work, as well as clustering validation methodologies. It is really useful if you are thinking about applying such algorithms and understanding the state-of-the-art.

_{}^{}

von Taikun C

•Feb 27, 2020

This course is very informative! It provides a skeleton for clustering analysis. It is well-design from basic concepts to advanced clustering techniques. I would definitely recommend this course for both clustering beginners and intermediate learners!

von Srinath R M

•Jul 10, 2018

Gave a very good understanding of cluster analysis - explaining all different methods and algorithms, the benefits and drawbacks of each. The tool ClusterEng looks very good and can help in a lot of situations. Thank so much

von Eric A S

•Dec 18, 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 Glushko O V

•Sep 19, 2017

Very informative lectures, wonderful assignments. This course isn't so easy but it gives you real knowledge and useful experience.

von Vijayashri B

•Nov 07, 2019

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

von Ian W

•Aug 20, 2018

Nice lecture.

The programming assignment is difficult, more instructions could be provided.

von Bhargavi K

•Apr 04, 2020

it was a really good experience. this course has given me good exposure to data mining

von Darren

•Sep 25, 2017

A very good course, it gives me a general idea of how clustering algorithm work.

von Tanan K

•Oct 10, 2017

Very intense and required complex thinking and programming skill

von Vasco D S N C D

•Nov 22, 2017

Excellent overview of many clustering algorithms!

von Pavan G

•Oct 02, 2017

Explained with nice examples

von Leela P

•Jan 16, 2017

Very useful and well taught

von AJETUNMOBI O

•May 01, 2017

Clustering demytified

von Ankit

•Feb 12, 2020

Fantastic course

- KI für alle
- Vorstellung von TensorFlow
- Neuronale Netzwerke und Deep Learning
- Algorithmen, Teil 1
- Algorithmen, Teil 2
- Maschinelles Lernen
- Maschinelles Lernen mit Python
- Maschinelles Lernen mittels Sas Viya
- R-Programmierung
- Einführung in die Programmierung mit Matlab
- Datenanalyse mit Python
- AWS-Grundlagen: Mit der Cloud vertraut werden
- Grundlagen der Google Cloud-Plattform
- Engineering für Site-Funktionssicherheit
- Englisch im Berufsleben
- Die Wissenschaft des Wohlbefindens
- Lernen lernen
- Finanzmärkte
- Hypothesenüberprüfung im öffentlichen Gesundheitswesen
- Grundlagen für Führungsstärke im Alltag

- Deep Learning
- Python für alle
- Data Science
- Angewandte Datenwissenschaft mit Python
- Geschäftsgründungen
- Architektur mit der Google Cloud-Plattform
- Datenengineering in der Google Cloud-Plattform
- Von Excel bis MySQL
- Erweiterte maschinelles Lernen
- Mathematik für maschinelles Lernen
- Selbstfahrende Autos
- Blockchain-Revolution für das Unternehmen
- Unternehmensanalytik
- Excel-Kenntnisse für Beruf
- Digitales Marketing
- Statistische Analyse mit R im öffentlichen Gesundheitswesen
- Grundlagen der Immunologie
- Anatomie
- Innovationsmanagement und Design Thinking
- Grundlagen positiver Psychologie