Skip to main navigation
Skip to search
Skip to main content
Experts@Minnesota Home
Home
Profiles
Research units
University Assets
Projects and Grants
Research output
Press/Media
Datasets
Activities
Fellowships, Honors, and Prizes
Search by expertise, name or affiliation
Clustering very large data sets with principal direction divisive partitioning
D. Littau,
D. Boley
Computer Science and Engineering
Research output
:
Chapter in Book/Report/Conference proceeding
›
Chapter
11
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Clustering very large data sets with principal direction divisive partitioning'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Engineering & Materials Science
Data storage equipment
100%
Clustering algorithms
37%
Scalability
33%