Almost Linear Time Consistent Mode Estimation and Quick Shift Clustering
By: Sajjad Hashemian
Potential Business Impact:
Finds patterns in huge amounts of information fast.
In this paper, we propose a method for density-based clustering in high-dimensional spaces that combines Locality-Sensitive Hashing (LSH) with the Quick Shift algorithm. The Quick Shift algorithm, known for its hierarchical clustering capabilities, is extended by integrating approximate Kernel Density Estimation (KDE) using LSH to provide efficient density estimates. The proposed approach achieves almost linear time complexity while preserving the consistency of density-based clustering.
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