#25 Hierarchical Clustering - Agglomerative & Divisive Algorithm |DM|

Summary

In this video, the concept of hierarchical clustering in data mining is explained, with a focus on its two main methods: agglomerative and divisive clustering. Agglomerative clustering works by merging data from the bottom to the top, using methods like single linkage, complete linkage, and average linkage to calculate similarities. On the other hand, divisive clustering is a top-to-bottom approach, splitting data iteratively until each data item forms its own cluster. The process is visually represented by a dendrogram, a tree-like structure that tracks the sequence of merges and splits in hierarchical clustering.

Takeaways

• 😀 Hierarchical clustering is a data mining technique that creates a hierarchy of clusters.
• 😀 The hierarchical clustering process is represented by a tree structure called a dendrogram.
• 😀 There are two main methods of hierarchical clustering: agglomerative and divisive.
• 😀 In the agglomerative method, clustering starts from the bottom (individual data points) and moves up by merging similar clusters.
• 😀 In the divisive method, clustering starts from the top (all data points in a single cluster) and moves down by splitting into smaller clusters.
• 😀 Agglomerative clustering merges data based on calculated similarity, repeating the process until one cluster remains.
• 😀 Divisive clustering splits data iteratively until each data point forms its own cluster.
• 😀 Agglomerative clustering operates in three modes: single linkage (min similarity), complete linkage (max similarity), and average linkage (average similarity).
• 😀 A dendrogram visually represents both the merging process (agglomerative) and the splitting process (divisive).
• 😀 Hierarchical clustering helps to understand relationships between data points through a clear, tree-like structure.
• 😀 The video encourages viewers to ask questions in the comments if they have any doubts about the topic.

Q & A

What is hierarchical clustering?

Hierarchical clustering is a method of clustering data where objects are grouped based on their similarities into a tree-like structure called a dendrogram. It is used in data mining to identify relationships and group similar data points.

What are the two methods of hierarchical clustering?

The two methods of hierarchical clustering are agglomerative and divisive. Agglomerative starts with individual data points and merges them, while divisive starts with all data points in one cluster and splits them.

How does the agglomerative method of hierarchical clustering work?

In the agglomerative method, clustering begins at the bottom where each data point is a separate cluster. These clusters are then merged based on their similarity until all data points belong to a single cluster.

What is a dendrogram?

A dendrogram is a tree-like diagram that represents the sequence of merges or splits in hierarchical clustering. It visualizes the structure and relationships of the clusters throughout the process.

What is the role of similarity in agglomerative clustering?

In agglomerative clustering, similarity is used to determine which clusters should be merged. Data points or clusters with higher similarity are merged first.

What are the three linkage methods used in agglomerative clustering?

The three linkage methods in agglomerative clustering are single linkage (based on minimum similarity), complete linkage (based on maximum similarity), and average linkage (based on average similarity between clusters).

How does the divisive method differ from the agglomerative method?

In divisive clustering, the process starts with all data points in a single cluster and then splits the cluster iteratively based on dissimilarity, whereas in agglomerative clustering, the process starts with individual data points and merges them.

What does the divisive method aim to achieve?

The divisive method aims to split a single large cluster into smaller clusters until each data point becomes its own separate cluster. This process is top to bottom, opposite to agglomerative clustering.

What is the direction of clustering in the agglomerative method?

The direction of clustering in the agglomerative method is from bottom to top, where individual data points are merged into clusters based on similarity, progressing toward a single cluster.

Why is hierarchical clustering useful in data mining?

Hierarchical clustering is useful in data mining because it helps organize data into meaningful clusters, revealing underlying structures and relationships. It’s particularly helpful for exploring and analyzing complex datasets where the groupings are not known beforehand.