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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Big Data Abstraction Through Multiagent Systems,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.

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Introduction,lid representative subsets of original data and feature sets. All further data mining analysis can be based only on these representative subsets leading to significant reduction in storage space and time. Another important direction is to compress the data by some manner and operate in the compresse

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Data Mining Paradigms, data mining. We elaborate some important data mining tasks such as clustering, classification, and association rule mining that are relevant to the content of the book. We discuss popular and representative algorithms of partitional and hierarchical data clustering. In classification, we discuss th

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Optimal Dimensionality Reduction,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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Big Data Abstraction Through Multiagent Systems,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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