Titlebook: Data Preprocessing in Data Mining; Salvador García,Julián Luengo,Francisco Herrera Book 2015 Springer International Publishing Switzerland

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Book 2015ctly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to ana

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Data Reduction, Mining is the “curse of dimensionality”, related with the usual high amount of attributes in data. Section?. deals with this problem. Data sampling and data simplification are introduced in Sects.?. and ., respectively, providing the basic notions on these topics for further analysis and explanation in subsequent chapters of the book.

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https://doi.org/10.1007/978-1-4613-0429-6accuracies are corrected. Section? . focuses in the latter task. Finally, some Data Mining applications involve some particular constraints like ranges for the data features, which may imply the normalization of the features (Sect.?.) or the transformation of the features of the data distribution (Sect.?.).

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https://doi.org/10.1007/978-1-4613-0429-6 problems that assume more complexity or hybridizations with respect to the classical learning paradigms. Finally, we establish the relationship between Data Preprocessing with Data Mining in Sect.?..

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https://doi.org/10.1007/978-1-4613-0429-6ter optimization models and derivatives methods related with feature selection, Sect.?. provides a summary on related and advanced topics, such as feature construction and feature extraction. An enumeration of some comparative experimental studies conducted in the specialized literature is included in Sect.?..

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Introduction, problems that assume more complexity or hybridizations with respect to the classical learning paradigms. Finally, we establish the relationship between Data Preprocessing with Data Mining in Sect.?..