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This book focuses on dealing with big-scale data, a space commonly known as data mining. The book is cut up into three sections. The first provides with an introduction to statistical parts of data mining and machine learning and consists of functions to textual content material analysis, laptop intrusion detection, and hiding of information in digital info. The second half focuses on a spread of statistical methodologies which have confirmed to be environment friendly in data mining functions. These embrace clustering, classification, multivariate density estimation, tree-based methods, pattern recognition, outlier detection, genetic algorithms, and dimensionality low cost. The third half focuses on data visualization and covers factors of visualization of high-dimensional data, novel graphical strategies with a give consideration to human parts, interactive graphics, and data visualization using virtual reality. This book represents a radical cross half of internationally renowned thinkers who’re inventing methods for dealing with a model new data paradigm.
– Distinguished contributors who’re worldwide specialists in parts of data mining
– Consists of data mining approaches to non-numerical data mining along with textual content material data, Net guests data, and geographic data
– Extraordinarily topical discussions reflecting current contemplating on trendy technical factors, e.g. streaming data
– Discusses taxonomy of dataset sizes, computational complexity, and scalability typically ignored in most discussions
– Thorough dialogue of data visualization factors mixing statistical, human parts, and computational insights
· Distinguished contributors who’re worldwide specialists in factors of data mining
· Consists of data mining approaches to non-numerical data mining along with textual content material data, Net guests data, and geographic data
· Extraordinarily topical discussions reflecting current contemplating on trendy technical factors, e.g. streaming data
· Discusses taxonomy of dataset sizes, computational complexity, and scalability typically ignored in most discussions
· Thorough dialogue of data visualization factors mixing statistical, human parts, and computational insights
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