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Related Questions
- What are the key differences between density-based clustering algorithms such as DBSCAN and statistical-based outlier detection methods like LOF?
- How does the computational complexity of DBSCAN compare to that of isolation forest for outlier detection?
- Can you explain the trade-offs between the two approaches in terms of accuracy and computational cost?
- What are some scenarios where density-based clustering is more suitable than statistical-based outlier detection, and vice versa?
- How does the choice of distance metric affect the performance of density-based clustering and statistical-based outlier detection?
- Can you provide examples of real-world applications where density-based clustering and statistical-based outlier detection have been used effectively?
- What are some emerging techniques that combine the strengths of density-based clustering and statistical-based outlier detection, and how do they impact computational complexity?
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