Coursera
MOOC / Non-credit
0
Cluster Analysis in Data Mining
About this course
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.
B
75/100
CourseAsk score
- What the provider tells you
- 24/45
- Who stands behind it
- 35/35
- How complete the listing is
- 16/20
Scores how much the provider publishes and who stands behind it — not how well it is taught.
What you'll learn
- understand basic concepts of cluster analysis
- apply partitioning methods like k-means
- utilize hierarchical clustering methods such as BIRCH
- implement density-based methods including DBSCAN and OPTICS
- evaluate clustering quality and validation
Data Analysis
#machine learning
#data mining
#optics
#cluster analysis
#data evaluation
#k-means
#hierarchical clustering
#BIRCH
#DBSCAN
#clustering validation
$79.00
Price shown by Coursera — confirm on their site.
Enroll on CourseraYou'll be redirected to Coursera to complete enrollment.
- Listed & compared by CourseAsk
- English · 0
Coursera
Udemy
Compared on these lists
Where this course ranks against the alternatives.