Enhancing density-based data reduction using entropy.

D Huang; Tommy W S Chow

doi:10.1162/089976606775093927

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Enhancing density-based data reduction using entropy.

D Huang·Tommy W S Chow

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Abstract

Data reduction algorithms determine a small data subset from a given large data set. In this article, new types of data reduction criteria, based on the concept of entropy, are first presented. These criteria can evaluate the data reduction performance in a sophisticated and comprehensive way. As a result, new data reduction procedures are developed. Using the newly introduced criteria, the proposed data reduction scheme is shown to be efficient and effective. In addition, an outlier-filtering strategy, which is computationally insignificant, is developed. In some instances, this strategy can substantially improve the performance of supervised data analysis. The proposed procedures are compared with related techniques in two types of application: density estimation and classification. Extensive comparative results are included to corroborate the contributions of the proposed algorithms.

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Algorithms Computer Simulation Entropy Pattern Recognition, Automated Comparative Study Journal Article Research Support, Non-U.S. Gov't

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D Huang, Tommy W S Chow. 2006. Enhancing density-based data reduction using entropy.. https://doi.org/10.1162/089976606775093927

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