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What method is used to update the proximity matrix after merging the two closest clusters?
What method is used to update the proximity matrix after merging the two closest clusters?
In the context of cluster similarity, what does MIN refer to?
In the context of cluster similarity, what does MIN refer to?
Which method uses squared error as part of its objective function?
Which method uses squared error as part of its objective function?
What is the name for the distance between centroids in the context of cluster similarity?
What is the name for the distance between centroids in the context of cluster similarity?
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In the given context, what does MAX refer to?
In the given context, what does MAX refer to?
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What is used in Ward's Method as part of its objective function?
What is used in Ward's Method as part of its objective function?
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Which method involves merging clusters based on single linkage?
Which method involves merging clusters based on single linkage?
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What is the term 'Single Link' associated with?
What is the term 'Single Link' associated with?
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What is used in Ward’s Method to measure inter-cluster similarity?
What is used in Ward’s Method to measure inter-cluster similarity?
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What does 'Group Average' represent in the context of cluster similarity?
What does 'Group Average' represent in the context of cluster similarity?
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What is the main visual representation of hierarchical clustering?
What is the main visual representation of hierarchical clustering?
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In hierarchical clustering, what does agglomerative clustering involve?
In hierarchical clustering, what does agglomerative clustering involve?
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What distinguishes different algorithms in hierarchical clustering?
What distinguishes different algorithms in hierarchical clustering?
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What is the key operation in the computation of the proximity of two clusters in hierarchical clustering?
What is the key operation in the computation of the proximity of two clusters in hierarchical clustering?
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What is the main advantage of hierarchical clustering?
What is the main advantage of hierarchical clustering?
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What type of clustering involves splitting a cluster until each cluster contains a single point?
What type of clustering involves splitting a cluster until each cluster contains a single point?
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What does an agglomerative clustering algorithm repeat until only a single cluster remains?
What does an agglomerative clustering algorithm repeat until only a single cluster remains?
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Which type of clustering starts with individual points as clusters and then merges the closest pair of clusters at each step?
Which type of clustering starts with individual points as clusters and then merges the closest pair of clusters at each step?
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What is the key operation in the computation of the proximity of two clusters in hierarchical clustering?
What is the key operation in the computation of the proximity of two clusters in hierarchical clustering?
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In hierarchical clustering, what does agglomerative clustering involve?
In hierarchical clustering, what does agglomerative clustering involve?
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What method is used to update the proximity matrix after merging the two closest clusters?
What method is used to update the proximity matrix after merging the two closest clusters?
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What is used in Ward’s Method to measure inter-cluster similarity?
What is used in Ward’s Method to measure inter-cluster similarity?
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What does 'Group Average' represent in the context of cluster similarity?
What does 'Group Average' represent in the context of cluster similarity?
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Which method involves merging clusters based on single linkage?
Which method involves merging clusters based on single linkage?
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What type of clustering involves splitting a cluster until each cluster contains a single point?
What type of clustering involves splitting a cluster until each cluster contains a single point?
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What does 'MAX' refer to in hierarchical clustering?
What does 'MAX' refer to in hierarchical clustering?
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What distinguishes different algorithms in hierarchical clustering?
What distinguishes different algorithms in hierarchical clustering?
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In the given context, when merging the two closest clusters (C2 and C5) and updating the proximity matrix, what is the key operation?
In the given context, when merging the two closest clusters (C2 and C5) and updating the proximity matrix, what is the key operation?
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What type of clustering involves starting with individual points as clusters and then merging the closest pair of clusters at each step?
What type of clustering involves starting with individual points as clusters and then merging the closest pair of clusters at each step?
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In the context of cluster similarity, what does 'MIN' refer to?
In the context of cluster similarity, what does 'MIN' refer to?
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What is used in Ward's Method to measure inter-cluster similarity?
What is used in Ward's Method to measure inter-cluster similarity?
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Which method uses squared error as part of its objective function?
Which method uses squared error as part of its objective function?
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What does 'Group Average' represent in the context of cluster similarity?
What does 'Group Average' represent in the context of cluster similarity?
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What distinguishes different algorithms in hierarchical clustering?
What distinguishes different algorithms in hierarchical clustering?
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What is the name for the distance between centroids in the context of cluster similarity?
What is the name for the distance between centroids in the context of cluster similarity?
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What is used in Ward's Method as part of its objective function?
What is used in Ward's Method as part of its objective function?
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'Single Link' is associated with which method of merging clusters?
'Single Link' is associated with which method of merging clusters?
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Study Notes
Cluster Similarity Methods
- MIN refers to the minimum distance between two clusters.
- MAX refers to the maximum distance between two clusters.
- The distance between centroids is referred to as the centroid distance.
Hierarchical Clustering
- Agglomerative clustering involves merging clusters until only a single cluster remains.
- Agglomerative clustering starts with individual points as clusters and then merges the closest pair of clusters at each step.
- The key operation in the computation of the proximity of two clusters is updating the proximity matrix after merging the two closest clusters.
- The main advantage of hierarchical clustering is its visual representation, which is typically a dendrogram.
Clustering Algorithms
- Single Linkage method involves merging clusters based on the minimum distance between two clusters.
- Ward's Method uses squared error as part of its objective function and measures inter-cluster similarity using the centroid distance.
- Group Average represents the average distance between all pairs of points in each cluster.
Clustering Types
- Divisive clustering involves splitting a cluster until each cluster contains a single point.
- Agglomerative clustering involves merging clusters until only a single cluster remains.
- Hierarchical clustering involves either agglomerative or divisive clustering.
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Description
This quiz covers the concept of hierarchical clustering in data analysis. It includes topics such as nested clusters, dendrogram visualization, and strengths of hierarchical clustering.
