國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Data preprocessing in data mining

Garcia, Salvador.

Data preprocessing in data mining

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data preprocessing in data miningby Salvador Garcia, Julian Luengo, Francisco Herrera.
作者:	Garcia, Salvador.
其他作者:	Luengo, Julian.
出版者:	Cham :Springer International Publishing :2015.
面頁冊數:	xv, 320 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Data mining.
電子資源:	http://dx.doi.org/10.1007/978-3-319-10247-4
ISBN:	9783319102474 (electronic bk.)

Data preprocessing in data mining
Garcia, Salvador.

Data preprocessing in data mining[electronic resource] /by Salvador Garcia, Julian Luengo, Francisco Herrera. - Cham :Springer International Publishing :2015. - xv, 320 p. :ill., digital ;24 cm. - Intelligent systems reference library,v.721868-4394 ;. - Intelligent systems reference library ;v.24..

Introduction -- Data Sets and Proper Statistical Analysis of Data Mining Techniques -- Data Preparation Basic Models -- Dealing with Missing Values -- Dealing with Noisy Data -- Data Reduction -- Feature Selection -- Instance Selection -- Discretization -- A Data Mining Software Package Including Data Preparation and Reduction: KEEL.

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.

ISBN: 9783319102474 (electronic bk.)

Standard No.: 10.1007/978-3-319-10247-4doiSubjects--Topical Terms:

184440
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.3

Data preprocessing in data mining
LDR:02842nmm a2200325 a 4500 001 462031
003 DE-He213
005 20150527110543.0
006 m d
007 cr nn 008maaau
008 151111s2015 gw s 0 eng d
020 $a 9783319102474 (electronic bk.)
020 $a 9783319102467 (paper)
024 7 $a 10.1007/978-3-319-10247-4 $2 doi
035 $a 978-3-319-10247-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.D343
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 006.3 $2 23
090 $a QA76.9.D343 $b G216 2015
100 1 $a Garcia, Salvador. $3 714549
245 1 0 $a Data preprocessing in data mining $h [electronic resource] / $c by Salvador Garcia, Julian Luengo, Francisco Herrera.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
300 $a xv, 320 p. : $b ill., digital ; $c 24 cm.
490 1 $a Intelligent systems reference library, $x 1868-4394 ; $v v.72
505 0 $a Introduction -- Data Sets and Proper Statistical Analysis of Data Mining Techniques -- Data Preparation Basic Models -- Dealing with Missing Values -- Dealing with Noisy Data -- Data Reduction -- Feature Selection -- Instance Selection -- Discretization -- A Data Mining Software Package Including Data Preparation and Reduction: KEEL.
520 $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.
650 0 $a Data mining. $3 184440
650 0 $a Electronic data processing $x Data preparation. $3 224687
650 1 4 $a Engineering. $3 210888
650 2 4 $a Computational Intelligence. $3 338479
650 2 4 $a Image Processing and Computer Vision. $3 274051
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
700 1 $a Luengo, Julian. $3 714550
700 1 $a Herrera, Francisco. $3 274681
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Intelligent systems reference library ; $v v.24. $3 558591
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-10247-4
950 $a Engineering (Springer-11647)