2 citations · 2 across the 7 of their papers we have counts for
8 papers
Chamfer-Linkage for Hierarchical Agglomerative Clustering
Kishen N Gowda, Willem Fletcher, MohammadHossein Bateni +4
Hierarchical Agglomerative Clustering (HAC) is a widely-used clustering method based on repeatedly merging the closest pair of clusters, where inter-cluster distances are determine…
JAG: Joint Attribute Graphs for Filtered Nearest Neighbor Search
Haike Xu, Guy Blelloch, Laxman Dhulipala +3
Despite filtered nearest neighbor search being a fundamental task in modern vector search systems, the performance of existing algorithms is highly sensitive to query selectivity a…
PiPNN: Ultra-Scalable Graph-Based Nearest Neighbor Indexing
Tobias Rubel, Richard Wen, Laxman Dhulipala +3
The fastest indexes for Approximate Nearest Neighbor Search today are also the slowest to build: graph-based methods like HNSW and Vamana achieve state-of-the-art query performance…
Parallel Hierarchical Agglomerative Clustering in Low Dimensions
MohammadHossein Bateni, Laxman Dhulipala, Willem Fletcher +4
Hierarchical Agglomerative Clustering (HAC) is an extensively studied and widely used method for hierarchical clustering in based on repeatedly merging the closest p…
Efficient Centroid-Linkage Clustering
MohammadHossein Bateni, Laxman Dhulipala, Willem Fletcher +4
We give an efficient algorithm for Centroid-Linkage Hierarchical Agglomerative Clustering (HAC), which computes a -approximate clustering in roughly time. We ob…
Dynamic PageRank: Algorithms and Lower Bounds
Rajesh Jayaram, Jakub Łącki, Slobodan Mitrović +2
We consider the PageRank problem in the dynamic setting, where the goal is to explicitly maintain an approximate PageRank vector for a graph under a sequence of…
It's Hard to HAC with Average Linkage!
MohammadHossein Bateni, Laxman Dhulipala, Kishen N Gowda +3
Average linkage Hierarchical Agglomerative Clustering (HAC) is an extensively studied and applied method for hierarchical clustering. Recent applications to massive datasets have d…
Unleashing Graph Partitioning for Large-Scale Nearest Neighbor Search
Lars Gottesbüren, Laxman Dhulipala, Rajesh Jayaram +1
We consider the fundamental problem of decomposing a large-scale approximate nearest neighbor search (ANNS) problem into smaller sub-problems. The goal is to partition the input po…