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Related Questions
- What is the primary difference between Euclidean and non-Euclidean distance measures in clustering?
- Can non-Euclidean distance measures like Cosine distance improve the accuracy of k-means clustering on text or high-dimensional data?
- How does the choice of distance metric impact the stability of the k-means algorithm, especially with non-Euclidean measures?
- What are the key characteristics of the Cosine distance measure, and how does it compare to the Euclidean distance?
- Can k-means clustering with Cosine distance be used for real-valued data, or is it more suited to binary or categorical data?
- How do the computational costs and speeds compare between k-means clustering with Euclidean distance versus Cosine distance?
- Are there any situations or applications where the use of non-Euclidean distance measures like Cosine distance is more desirable than traditional Euclidean distance in k-means clustering?
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