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Hierarchical
Hierarchical Clustering Hierarchical clustering is a method used in data mining for grouping data points into clusters based on their similarities. It invol...
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Hierarchical Clustering Hierarchical clustering is a method used in data mining for grouping data points into clusters based on their similarities. It invol...
Hierarchical Clustering
Hierarchical clustering is a method used in data mining for grouping data points into clusters based on their similarities. It involves constructing a hierarchy of clusters by iteratively merging or dividing clusters based on the distance or similarity between them.
How it works:
Start with one cluster: Each data point is initially assigned to a single cluster.
Merge similar clusters: If the distance between two clusters is below a specified threshold, they are merged into a single cluster.
Divide dissimilar clusters: If the distance between two clusters is greater than the threshold, they are divided into two separate clusters.
Repeat steps 2 and 3: Continue merging or dividing clusters until there is only one cluster left.
Benefits of hierarchical clustering:
Identifies natural clusters: It helps identify clusters that naturally exist in the data.
Provides a hierarchical representation: The clusters are arranged in a hierarchy, which can be visualized and interpreted.
Can handle high-dimensional data: It can handle data with a large number of features.
Examples:
Imagine a dataset of movie ratings, where movies are clustered into genres.
In a healthcare dataset, patients can be grouped based on their medical records and symptoms.
In a market analysis dataset, items can be clustered based on their prices and features.
Applications of hierarchical clustering:
Market research: Identifying customer groups and market segments.
Science: Grouping biological sequences and genes.
Medical diagnosis: Clustering patient data to identify diseases.
Business intelligence: Clustering sales data to identify trends and patterns