Data Preprocessing in Data Mining von Garc�a, Salvador/ Luengo, Juli�n/ Herrera, Francisco: Brand New Hardcover (2014)

Beschreibung

2015 edition. 336 pages. 9.25x6.25x0.75 inches. In Stock. Bestandsnummer des Verk�ufers x-331910246X

Verk�ufer kontaktieren

Diesen Artikel melden

Bibliografische Details

Titel: Data Preprocessing in Data Mining

Verlag: Springer-Verlag New York Inc

Erscheinungsdatum: 2014

Einband: Hardcover

Zustand: Brand New

�ber diesen Titel

Inhaltsangabe:

Data Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly 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 analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data.

This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying the techniques proposed in the specialized literature, is given.Each chapter is a stand-alone guide to a particular data preprocessing topic, from basic concepts and detailed descriptions of classical algorithms, to an incursion of an exhaustive catalog of recent developments. The in-depth technical descriptions make this book suitable for technical professionals, researchers, senior undergraduate and graduate students in data science, computer science and engineering.

Von der hinteren Coverseite:

Data Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly 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 analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data.

This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying the techniques proposed in the specialized literature, is given.Each chapter is a stand-alone guide to a particular data preprocessing topic, from basic concepts and detailed descriptions of classical algorithms, to an incursion of an exhaustive catalog of recent developments. The in-depth technical descriptions make this book suitable for technical professionals, researchers, senior undergraduate and graduate students in data science, computer science and engineering.

��ber diesen Titel� kann sich auf eine andere Ausgabe dieses Titels beziehen.

Verwandte Artikel zu Data Preprocessing in Data Mining

Data Preprocessing in Data Mining

Garc�a, Salvador/ Luengo, Juli�n/ Herrera, Francisco

Neu - Hardcover

Unternehmensdaten des Verk�ufers