Titlebook: Compression Schemes for Mining Large Datasets; A Machine Learning P T. Ravindra Babu,M. Narasimha Murty,S.V. Subrahman Book 2013 Springer-V

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书目名称Compression Schemes for Mining Large Datasets读者反馈学科排名

纹章

Enzyme

,1919–1923 “Independence or Death!”,vide a better classification accuracy than the original dataset. In this direction, we implement the proposed scheme on two large datasets, one with binary-valued features and the other with float-point-valued features. At the end of the chapter, we provide bibliographic notes and a list of referenc

荒唐

不怕任性

Product Mix and Diversification,ow the divide-and-conquer approach of multiagent systems improves handling huge datasets. We propose four multiagent systems that can help generating abstraction with big data. We provide suggested reading and bibliographic notes. A list of references is provided in the end.

洁净

2191-6586 e in generating abstraction; reviews optimal prototype selection using genetic algorithms; suggests possible ways of dealing with big data problems using multiagent systems.978-1-4471-7055-6978-1-4471-5607-9Series ISSN 2191-6586 Series E-ISSN 2191-6594

洁净

Data Mining Paradigms,n intermediate representation. The discussion on classification includes topics such as incremental classification and classification based on intermediate abstraction. We further discuss frequent-itemset mining with two directions such as divide-and-conquer itemset mining and intermediate abstracti

FOVEA

Dimensionality Reduction by Subsequence Pruning,earest neighbors. This results in lossy compression in two levels. Generating compressed testing data forms an interesting scheme too. We demonstrate significant reduction in data and its working on large handwritten digit data. We provide bibliographic notes and references at the end of the chapter

FLUSH

Data Compaction Through Simultaneous Selection of Prototypes and Features,vide a better classification accuracy than the original dataset. In this direction, we implement the proposed scheme on two large datasets, one with binary-valued features and the other with float-point-valued features. At the end of the chapter, we provide bibliographic notes and a list of referenc

Ornament