Corporate Hierarchical Clustering Training Course

1. Overview of hierarchical clustering
   • Definition and purpose of hierarchical clustering
   • Comparison to other clustering methods
2. Grouping similar data points into clusters
   • Identifying similarities and dissimilarities between data points
   • Forming clusters based on these similarities
3. Hierarchical structure of clusters
   • Hierarchical organization of clusters into a tree-like structure
   • Visual representation through dendrograms

1. Difference between supervised and unsupervised learning
   • Supervised learning: Using labeled data for training and prediction
   • Unsupervised learning: Finding patterns or structures in unlabeled data
2. Hierarchical clustering as an unsupervised learning method
   • Absence of labels in the data
   • Clustering without predefined class information
3. Finding patterns or structure in data without labeled information
   • Discovering inherent structures or relationships among data points
   • Extracting meaningful insights from unstructured data

1. Advantages of hierarchical clustering
   • Flexibility in handling different data types and distances
   • Visualization of hierarchical relationships and subgroups
2. Applications of hierarchical clustering
   • Market segmentation and customer profiling
   • Biological taxonomy and evolutionary relationships
   • Image segmentation and object recognition
3. Handling unknown underlying structure and unknown number of clusters
   • Ability to explore data structure without assumptions
   • Determining the number of clusters from the hierarchical structure

Agglomerative Hierarchical Clustering:

1. Starting with each data point as a separate cluster
   • Assigning each data point to an individual cluster
2. Iteratively merging the most similar clusters
   • Measuring similarity between clusters using linkage criteria
   • Merging clusters with the highest similarity until a single cluster is formed
3. Forming a single cluster containing all data points
   • Generating a hierarchical structure of nested clusters

b) Divisive Hierarchical Clustering:

1. Starting with a single cluster containing all data points
   • Considering all data points as part of a single cluster
2. Recursively dividing the cluster into smaller subclusters
   • Finding the optimal splitting point based on a dissimilarity measure
   • Dividing the cluster into two subclusters at each step
3. Each data point eventually in its own cluster
   • Continuously dividing the clusters until each data point forms an individual cluster

Setting up the Example:

1. Selecting a dataset
   • Choosing a dataset suitable for clustering analysis
2. Preparing the data for clustering
   • Handling missing values and outliers
   • Scaling or normalizing the data if necessary

b) Creating a Proximity Matrix:

1. Calculating the similarity or dissimilarity between data points
   • Choosing a distance metric (e.g., Euclidean distance, cosine similarity)
   • Computing the pairwise distances between data points
2. Constructing a proximity matrix
   • Representing the distances in a matrix format

c) Steps to Perform Hierarchical Clustering:

1. Initializing each data point as a separate cluster
   • Assigning each data point to its own initial cluster
2. Calculating proximity between clusters
   • Measuring the similarity between clusters using linkage criteria
3. Iteratively merging or dividing clusters based on linkage criteria
   • Repeating the merging or dividing process until desired clusters are obtained

1. Dendrogram analysis
   • Visualizing the hierarchical structure using dendrograms
   • Analyzing the heights of the dendrogram branches
2. Silhouette score
   • Calculating silhouette coefficients for different numbers of clusters
   • Selecting the number of clusters with the highest silhouette score
3. Gap statistic
   • Comparing the within-cluster dispersion to its expected value
   • Determining the number of clusters with the largest gap statistic