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Related Questions
- How does data normalization affect the clustering algorithm's ability to identify meaningful patterns in the data?
- What are some common techniques for handling missing values in data preprocessing, and how do they impact clustering performance?
- Can you explain the concept of feature scaling and its importance in clustering, and how it can be achieved?
- How does data transformation, such as log transformation or standardization, impact the clustering results, and when is it most useful?
- What are some best practices for selecting the most relevant features for clustering, and how can data preprocessing help with this process?
- How can data preprocessing techniques, such as dimensionality reduction, be used to improve clustering performance and reduce computational complexity?
- What are some common pitfalls to avoid when performing data preprocessing for clustering, and how can they be mitigated?
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