Fast attribute-based unsupervised and supervised table clustering using P-Trees

Arjun G. Roy, Matthew Piehl, Arijit Chatterjee, Mohammad K. Hossain, Bryan Mesich, Tingda Lu, Greg Wettstein, Amal Perera, William Perrizo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Since the advent of digital image technology and remote sensing imagery (RSI), massive amount of image data has been collected worldwide. For example, since 1972, NASA and U.S. Geological Survey through the Landsat Data Continuity Mission, has been capturing images of Earth down to 15 meters resolution. Since image clustering is time-consuming, much of this data is archived even before analysis. In this paper, we propose a novel and extremely fast algorithm called FAUST-P or Fast Attribute-based Unsupervised and Supervised Table Clustering for images. Our algorithm is based on Predicate-Trees which are compressed, lossless and data-mining-ready data structures. Without compromising much on the accuracy, our algorithm is fast and can be effectively used in high-speed image data analysis.

Original languageEnglish (US)
Title of host publicationProceedings of the ISCA 20th International Conference on Software Engineering and Data Engineering, SEDE 2011
Pages139-142
Number of pages4
StatePublished - Dec 1 2011
Externally publishedYes
Event20th International Conference on Software Engineering and Data Engineering, SEDE 2011 - Las Vegas, NV, United States
Duration: Jun 20 2011Jun 22 2011

Publication series

NameProceedings of the ISCA 20th International Conference on Software Engineering and Data Engineering, SEDE 2011

Conference

Conference20th International Conference on Software Engineering and Data Engineering, SEDE 2011
Country/TerritoryUnited States
CityLas Vegas, NV
Period6/20/116/22/11

Fingerprint

Dive into the research topics of 'Fast attribute-based unsupervised and supervised table clustering using P-Trees'. Together they form a unique fingerprint.

Cite this