The most popular and extensively used method for analysing social network data is hierarchical clustering. Nodes are compared to one another using this method based on their similarity. Larger groups are formed by combining groups of nodes that are comparable in some way.
Hierarchical clustering can be divided into two types: divisive (top-down) and agglomerative (bottom-up) (bottom-up).
Another unsupervised learning approach, hierarchical clustering, is used to group together unlabeled data points with comparable features.
Divisive and Agglomerative hierarchical clustering are the two types of hierarchical clustering. We assign all of the observations to a single cluster in the divisive or top-down clustering technique, and then partition the cluster into two least similar clusters.
A hierarchical model is a data analysis structure in which the data is structured into a tree-like structure or one that uses multilevel (hierarchical) modelling. The first is concerned with both a theoretical framework and the placement of individual items under categories that may or may not be related.
Learner's Ratings
4.4
Overall Rating
73%
16%
6%
4%
1%
Reviews
S
Suman Bhattacharya
4
It's good to understand
V
vaibhav
5
best
A
Anjali
5
Excellent explanation
A
ashif mahiboob yadwad
5
Very good
S
SHAIKH TOHEED
5
good but practical knowledge not perfect
P
PUSHPAK DILLIP MALI
4
The content and teaching style is excellent.
But there is slight issue is that video quality is kind of poor the dashboard is blur on 720mp on PC.
S
Sayali Jadhav
4
Very useful all data
S
Syed Mohammad Qaiser Rizvi
5
Way of teaching is very good. I want to get PPT which he is using to teach us ?
A
Anuja Bagadi
5
It is a very interactive and useful course..
K
Kashinath Myakale
5
All courses is very useful for which are looking free courses and gaining knowledge
Share a personalized message with your friends.