Fast attribute-based table clustering using Predicate-Trees: A vertical data mining approach
Issue title: Special Supplement Issue in Section A and B: Selected Papers from the ISCA International Conference on Software Engineering and Data Engineering, 2011
Affiliations: Department of Computer Science and Operations Research, North Dakota State University, Fargo, ND, USA | Department of Computer Science, Winona State University, Winona, MN, USA
Correspondence:
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Corresponding author: Arjun G. Roy, Department of Computer Science and Operations Research, North Dakota State University, Fargo, ND 58102, USA. Tel.: +1 701 231 8562; E-mail: [email protected].
Abstract: With technological advancements, massive amount of data is being collected in various domains. For instance, since the advent of digital image technology and remote sensing imagery (RSI), NASA and U.S. Geological Survey through the Landsat Data Continuity Mission, has been capturing images of Earth down to 15 meters resolution. Likewise, consider the Internet, where, growth of social media, blog Web sites , etc. generates exponential amount of textual data on a daily basis. Since clustering of data is time-consuming, much of these data is archived even before proper analysis. In this paper, we propose two novel and extremely fast algorithms called imgFAUST or Fast Attribute-based Unsupervised and Supervised Table Clustering for images and a variation called docFAUST for textual data. Both these algorithms are based on Predicate-Trees which are compressed, lossless and data-mining-ready data structures. Without compromising much on the accuracy, our algorithms are fast and can be effectively used in high-speed image data and document analysis.
Keywords: Data mining, Predicate Trees, vertical data processing, big data
