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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摘要記錄

Trans-European Telecommunication Networks, features in the given representation of patterns, we would still be able to generate an abstraction that is as accurate in classification as the one with original feature set. In this chapter, we propose a lossy compression scheme. We demonstrate its efficiency and accuracy on practical datasets. T

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Product Mix and Diversification,ucing the features include conventional feature selection and extraction methods, frequent item support-based methods, and optimal feature selection approaches. In earlier chapters, we discussed feature selection based on frequent items. In the present chapter, we combine a nonlossy compression sche

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Product Mix and Diversification,tems. Big data is characterized by huge volumes of data that are not easily amenable for generating abstraction; variety of data formats, data frequency, types of data, and their integration; real or near-real time data processing for generating business or scientific value depending on nature of da

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Compression Schemes for Mining Large Datasets978-1-4471-5607-9Series ISSN 2191-6586 Series E-ISSN 2191-6594

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978-1-4471-7055-6Springer-Verlag London 2013

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T. Ravindra Babu,M. Narasimha Murty,S.V. SubrahmanExamines all aspects of data abstraction generation using a least number of database scans.Discusses compressing data through novel lossy and non-lossy schemes.Proposes schemes for carrying out cluste