10- Cluster Evaluation

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What is the Silhouette index optimized with in a -medoids-like procedure?

Euclidean distance

What is considered better in terms of the Davies-Bouldin index?

A small Davies-Bouldin index

What does the Dunn index measure?

Cluster distance divided by maximum cluster diameter

What do the Gamma and Tau metrics compare to evaluate clustering?

Within-cluster distance to between-cluster distance Signup and view all the answers

What does the PBM Index consider for cluster validation?

Distance to cluster center divided by distance to total center Signup and view all the answers

What is one of the intrinsic cluster evaluation measures mentioned in the text?

Gap statistic Signup and view all the answers

What is the main difference between clustering and classification in terms of evaluation?

Clustering is not classification, so it cannot be evaluated the same way. Signup and view all the answers

Why is it not always possible to assume that good clusters correspond to the classes in real data?

In real data, labels are often not available, making it impossible to directly compare clusters to classes. Signup and view all the answers

How can the best matching between clusters and classes be determined?

One way is to use the Hungarian algorithm, although it is uncommon. Another way is to compare every cluster to every class. Signup and view all the answers

What is the concept of 'purity' in cluster evaluation?

Purity is a measure that assesses the quality of a cluster by how well it contains elements of a single class. Signup and view all the answers

Why might having every document as its own cluster not be an optimal clustering strategy?

Having every document as its own cluster may not be optimal because it can lead to high purity but may not reflect meaningful clustering. Signup and view all the answers

What are some challenges in supervised cluster evaluation?

Challenges include dealing with more clusters than classes, clusters containing multiple classes, and classes spanning multiple clusters. Signup and view all the answers

What is the purpose of Adjusted Rand Index (ARI) in cluster evaluation?

Adjusting for chance in evaluating the similarity between two clusterings. Signup and view all the answers

What is the limitation of Normalized Mutual Information (NMI) in cluster evaluation?

NMI tends to prefer solutions that split clusters too much. Signup and view all the answers

How is Variation of Information related to NMI in cluster evaluation?

Variation of Information is a variant of NMI and serves as a metric. Signup and view all the answers

Why is clustering text data considered challenging?

Text data clustering is challenging due to high dimensionality, sparse nature, and the influence of preprocessing methods. Signup and view all the answers

How does preprocessing impact the results of clustering text data?

Preprocessing, such as TF-IDF, significantly influences the clustering results. Signup and view all the answers

Why do traditional notions of 'distance' and 'density' not work well in text data clustering?

Traditional distance and density concepts do not align well with the characteristics of text data, which is high-dimensional and sparse. Signup and view all the answers