Data preprocessing in data mining:
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. Fur...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cham, Switzerland
Springer
[2015]
|
| Schriftenreihe: | Intelligent systems reference library
volume 72 |
| Schlagworte: | |
| Zusammenfassung: | 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 |
| Beschreibung: | xv, 320 Seiten Illustrationen 25 cm |
| ISBN: | 9783319102467 |
Internformat
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| 520 | 3 | |a 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 | |
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| discipline | Informatik |
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| institution | BVB |
| isbn | 9783319102467 |
| language | English |
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| spelling | García, Salvador Verfasser aut Data preprocessing in data mining Salvador García, Julián Luengo, Francisco Herrera Cham, Switzerland Springer [2015] xv, 320 Seiten Illustrationen 25 cm txt rdacontent n rdamedia nc rdacarrier Intelligent systems reference library volume 72 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 Datenverarbeitung (DE-588)4011152-0 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Engineering Data mining Computer vision Data Mining (DE-588)4428654-5 s Datenverarbeitung (DE-588)4011152-0 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 Luengo, Julián Verfasser (DE-588)1210302896 aut Herrera, Francisco Verfasser (DE-588)1235492125 aut Erscheint auch als Online-Ausgabe 978-3-319-10247-4 (DE-604)BV042195461 Intelligent systems reference library volume 72 (DE-604)BV035704685 72 |
| spellingShingle | García, Salvador Luengo, Julián Herrera, Francisco Data preprocessing in data mining Intelligent systems reference library Datenverarbeitung (DE-588)4011152-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Data Mining (DE-588)4428654-5 gnd |
| subject_GND | (DE-588)4011152-0 (DE-588)4193754-5 (DE-588)4428654-5 |
| title | Data preprocessing in data mining |
| title_auth | Data preprocessing in data mining |
| title_exact_search | Data preprocessing in data mining |
| title_exact_search_txtP | Data preprocessing in data mining |
| title_full | Data preprocessing in data mining Salvador García, Julián Luengo, Francisco Herrera |
| title_fullStr | Data preprocessing in data mining Salvador García, Julián Luengo, Francisco Herrera |
| title_full_unstemmed | Data preprocessing in data mining Salvador García, Julián Luengo, Francisco Herrera |
| title_short | Data preprocessing in data mining |
| title_sort | data preprocessing in data mining |
| topic | Datenverarbeitung (DE-588)4011152-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Data Mining (DE-588)4428654-5 gnd |
| topic_facet | Datenverarbeitung Maschinelles Lernen Data Mining |
| volume_link | (DE-604)BV035704685 |
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