大葉大學圖書館 |

Information granularity, big data, and computational intelligence[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	006.3
書名/作者:	Information granularity, big data, and computational intelligence/ edited by Witold Pedrycz, Shyi-Ming Chen.
其他作者:	Chen, Shyi-Ming.
出版者:	Cham : : Springer International Publishing :, 2015.
面頁冊數:	xi, 444 p. : : ill. (some col.), digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Granular computing.
標題:	Big data.
標題:	Computational intelligence.
標題:	Engineering.
標題:	Computational Intelligence.
標題:	Artificial Intelligence (incl. Robotics)
標題:	e-Commerce/e-business.
ISBN:	9783319082547 (electronic bk.)
ISBN:	9783319082530 (paper)
內容註:	From the Contents: Nearest Neighbor Queries on Big Data -- Information Mining for Big Information -- Information Granules Problem: An Efficient Solution of Real-Time Fuzzy Regression Analysis -- How to Understand Connections Based on Big Data: From Cliques to Flexible Granules.
摘要、提要註:	The recent pursuits emerging in the realm of big data processing, interpretation, collection and organization have emerged in numerous sectors including business, industry, and government organizations. Data sets such as customer transactions for a mega-retailer, weather monitoring, intelligence gathering, quickly outpace the capacities of traditional techniques and tools of data analysis. The 3V (volume, variability and velocity) challenges led to the emergence of new techniques and tools in data visualization, acquisition, and serialization. Soft Computing being regarded as a plethora of technologies of fuzzy sets (or Granular Computing), neurocomputing and evolutionary optimization brings forward a number of unique features that might be instrumental to the development of concepts and algorithms to deal with big data. This carefully edited volume provides the reader with an updated, in-depth material on the emerging principles, conceptual underpinnings, algorithms and practice of Computational Intelligence in the realization of concepts and implementation of big data architectures, analysis, and interpretation as well as data analytics. The book is aimed at a broad audience of researchers and practitioners including those active in various disciplines in which big data, their analysis and optimization are of genuine relevance. One focal point is the systematic exposure of the concepts, design methodology, and detailed algorithms. In general, the volume adheres to the top-down strategy starting with the concepts and motivation and then proceeding with the detailed design that materializes in specific algorithms and representative applications. The material is self-contained and provides the reader with all necessary prerequisites and, augments some parts with a step-by-step explanation of more advanced concepts supported by a significant amount of illustrative numeric material and some application scenarios to motivate the reader and make some abstract concepts more tangible.
電子資源:	http://dx.doi.org/10.1007/978-3-319-08254-7

Information granularity, big data, and computational intelligence[electronic resource] /
Information granularity, big data, and computational intelligence[electronic resource] /edited by Witold Pedrycz, Shyi-Ming Chen. - Cham :Springer International Publishing :2015. - xi, 444 p. :ill. (some col.), digital ;24 cm. - Studies in big data,v.82197-6503 ;. - Studies in big data ;v.7..

From the Contents: Nearest Neighbor Queries on Big Data -- Information Mining for Big Information -- Information Granules Problem: An Efficient Solution of Real-Time Fuzzy Regression Analysis -- How to Understand Connections Based on Big Data: From Cliques to Flexible Granules.

The recent pursuits emerging in the realm of big data processing, interpretation, collection and organization have emerged in numerous sectors including business, industry, and government organizations. Data sets such as customer transactions for a mega-retailer, weather monitoring, intelligence gathering, quickly outpace the capacities of traditional techniques and tools of data analysis. The 3V (volume, variability and velocity) challenges led to the emergence of new techniques and tools in data visualization, acquisition, and serialization. Soft Computing being regarded as a plethora of technologies of fuzzy sets (or Granular Computing), neurocomputing and evolutionary optimization brings forward a number of unique features that might be instrumental to the development of concepts and algorithms to deal with big data. This carefully edited volume provides the reader with an updated, in-depth material on the emerging principles, conceptual underpinnings, algorithms and practice of Computational Intelligence in the realization of concepts and implementation of big data architectures, analysis, and interpretation as well as data analytics. The book is aimed at a broad audience of researchers and practitioners including those active in various disciplines in which big data, their analysis and optimization are of genuine relevance. One focal point is the systematic exposure of the concepts, design methodology, and detailed algorithms. In general, the volume adheres to the top-down strategy starting with the concepts and motivation and then proceeding with the detailed design that materializes in specific algorithms and representative applications. The material is self-contained and provides the reader with all necessary prerequisites and, augments some parts with a step-by-step explanation of more advanced concepts supported by a significant amount of illustrative numeric material and some application scenarios to motivate the reader and make some abstract concepts more tangible.

ISBN: 9783319082547 (electronic bk.)

Standard No.: 10.1007/978-3-319-08254-7doiSubjects--Topical Terms:

602135
Granular computing.

LC Class. No.: QA76.9.S63

Dewey Class. No.: 006.3

Information granularity, big data, and computational intelligence[electronic resource] /
LDR:03311nam a2200325 a 4500 001 424382
003 DE-He213
005 20150513103847.0
006 m d
007 cr nn 008maaau
008 151119s2015 gw s 0 eng d
020 $a 9783319082547 (electronic bk.)
020 $a 9783319082530 (paper)
024 7 $a 10.1007/978-3-319-08254-7 $2 doi
035 $a 978-3-319-08254-7
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.S63
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 006.3 $2 23
090 $a QA76.9.S63 $b I43 2015
245 0 0 $a Information granularity, big data, and computational intelligence $h [electronic resource] / $c edited by Witold Pedrycz, Shyi-Ming Chen.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
300 $a xi, 444 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Studies in big data, $x 2197-6503 ; $v v.8
505 0 $a From the Contents: Nearest Neighbor Queries on Big Data -- Information Mining for Big Information -- Information Granules Problem: An Efficient Solution of Real-Time Fuzzy Regression Analysis -- How to Understand Connections Based on Big Data: From Cliques to Flexible Granules.
520 $a The recent pursuits emerging in the realm of big data processing, interpretation, collection and organization have emerged in numerous sectors including business, industry, and government organizations. Data sets such as customer transactions for a mega-retailer, weather monitoring, intelligence gathering, quickly outpace the capacities of traditional techniques and tools of data analysis. The 3V (volume, variability and velocity) challenges led to the emergence of new techniques and tools in data visualization, acquisition, and serialization. Soft Computing being regarded as a plethora of technologies of fuzzy sets (or Granular Computing), neurocomputing and evolutionary optimization brings forward a number of unique features that might be instrumental to the development of concepts and algorithms to deal with big data. This carefully edited volume provides the reader with an updated, in-depth material on the emerging principles, conceptual underpinnings, algorithms and practice of Computational Intelligence in the realization of concepts and implementation of big data architectures, analysis, and interpretation as well as data analytics. The book is aimed at a broad audience of researchers and practitioners including those active in various disciplines in which big data, their analysis and optimization are of genuine relevance. One focal point is the systematic exposure of the concepts, design methodology, and detailed algorithms. In general, the volume adheres to the top-down strategy starting with the concepts and motivation and then proceeding with the detailed design that materializes in specific algorithms and representative applications. The material is self-contained and provides the reader with all necessary prerequisites and, augments some parts with a step-by-step explanation of more advanced concepts supported by a significant amount of illustrative numeric material and some application scenarios to motivate the reader and make some abstract concepts more tangible.
650 0 $a Granular computing. $3 602135
650 0 $a Big data. $3 571002
650 0 $a Computational intelligence. $3 416528
650 1 4 $a Engineering. $3 372756
650 2 4 $a Computational Intelligence. $3 463962
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 463642
650 2 4 $a e-Commerce/e-business. $3 466144
700 1 $a Chen, Shyi-Ming. $3 468909
700 1 $a Pedrycz, Witold. $3 463960
710 2 $a SpringerLink (Online service) $3 463450
773 0 $t Springer eBooks
830 0 $a Studies in big data ; $v v.7. $3 602085
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-08254-7
950 $a Engineering (Springer-11647)