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Related Questions
- What are the key differences between k-means and hierarchical clustering algorithms in the context of text clustering in NLP?
- How do the assumptions of k-means clustering (e.g., spherical clusters, equal variance) affect its performance on text data?
- In what scenarios would hierarchical clustering be more suitable for text clustering, and vice versa?
- How do the choice of distance metric and linkage function impact the results of hierarchical clustering on text data?
- Can k-means clustering be used for text clustering with varying document lengths, and if so, what preprocessing steps are necessary?
- How do the number of clusters (k) and the choice of initialization method affect the performance of k-means clustering on text data?
- What are some common applications of text clustering in NLP, and how do k-means and hierarchical clustering algorithms contribute to these applications?
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