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Compression, Clustering and Pattern Discovery in Very High Dimensional Discrete-Attribute Datasets

This paper presents an efficient framework for error-bounded compression of high-dimensional discrete-attribute datasets. Such datasets, which frequently arise in a wide variety of applications, pose some of the most significant challenges in data analysis. Sub-sampling and compression are two key technologies for analyzing these datasets. The proposed framework, PROXIMUS, provides a technique for reducing large datasets into a much smaller set of representative patterns, on which traditional (expensive) analysis algorithms can be applied with minimal loss of accuracy.

Provided by: Purdue University Topic: Big Data Date Added: Jan 2011 Format: PDF

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