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Related Questions
- What are the key differences between hierarchical and k-means clustering algorithms in text clustering?
- How does the choice of similarity metric (e.g., cosine, Jaccard, or Euclidean distance) impact the balance between homogeneity and completeness in text clustering?
- Can you explain the trade-offs between using a clustering algorithm with a fixed number of clusters (k-means) versus one that determines the number of clusters automatically (e.g., DBSCAN)?
- How does the choice of text representation (e.g., bag-of-words, TF-IDF, or word embeddings) affect the performance of different clustering algorithms?
- What are some common techniques for evaluating the quality of text clusters, and how do they relate to homogeneity and completeness?
- Can you discuss the role of hyperparameter tuning in achieving a good balance between homogeneity and completeness in text clustering?
- How does the choice of clustering algorithm impact the interpretability of the resulting clusters, and what are some strategies for improving interpretability?
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