Poster: parallel algorithms for clustering and nearest neighbor search problems in high dimensions

Authors:
Logan Moon;Daniel Long;Shreyas Joshi;Vyomkesh Tripathi;Bo Xiao;George Biros
Affiliations:
The University of Texas at Austin, Austin, TX, USA;Georgia Institute of Technology, Atlanta, GA, USA;Georgia Institute of Technology, Atlanta, GA, USA;Georgia Institute of Technology, Atlanta, GA, USA;Georgia Institute of Technology, Atlanta, GA, USA;The University of Texas at Austin, Austin, TX, USA
Venue:
Proceedings of the 2011 companion on High Performance Computing Networking, Storage and Analysis Companion
Year:
2011

Citing 5
Cited 0

Parallel algorithms for hierarchical clustering

Parallel Computing
Data clustering: a review

ACM Computing Surveys (CSUR)
The X-tree: an index structure for high-dimensional data

Readings in multimedia computing and networking
An Efficient k-Means Clustering Algorithm: Analysis and Implementation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions

Communications of the ACM - 50th anniversary issue: 1958 - 2008

Quantified Score

Hi-index	0.00

Visualization

Abstract

Clustering and nearest neighbor searches in high dimensions are fundamental components of computational geometry, computational statistics, and pattern recognition. Despite the widespread need to analyze massive datasets, no MPI-based implementations are available to allow this analysis to be scaled to modern highly parallel platforms. We seek to develop a set of algorithms that will provide unprecedented scalability and performance for these fundamental problems.