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

Clustering methodology for symbolic ...

Billard, L. (1943-)

Clustering methodology for symbolic data /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Clustering methodology for symbolic data /Lynne Billard, Edwin Diday.
作者:	Billard, L.
其他作者:	Diday, E.,
出版者:	Hoboken, NJ :Wiley,2020.
面頁冊數:	vii, 340 p. :ill. ;24 cm.
標題:	Cluster analysis.
ISBN:	9780470713938 :

Clustering methodology for symbolic data /
Billard, L.1943-

Clustering methodology for symbolic data /Lynne Billard, Edwin Diday. - Hoboken, NJ :Wiley,2020. - vii, 340 p. :ill. ;24 cm. - Wiley series in computational statistics. - Wiley series in computational statistics..

Includes bibliographical references and index.

Introduction -- Symbolic data: basics -- Dissimilarity, similarity, and distance measures -- Dissimilarity, similarity, and distance measures: modal data -- General clustering techniques -- Partitioning techniques -- Divisive hierarchical clustering -- Agglomerative hierarchical clustering.

This book presents all of the latest developments in the field of clustering methodology for symbolic data--paying special attention to the classification methodology for multi-valued list, interval-valued and histogram-valued data methodology, along with numerous worked examples. The book also offers an expansive discussion of data management techniques showing how to manage the large complex dataset into more manageable datasets ready for analyses. Filled with examples, tables, figures, and case studies, Clustering Methodology for Symbolic Data begins by offering chapters on data management, distance measures, general clustering techniques, partitioning, divisive clustering, and agglomerative and pyramid clustering. Clustering Methodology for Symbolic Data will appeal to practitioners of symbolic data analysis, such as statisticians and economists within the public sectors. It will also be of interest to postgraduate students of, and researchers within, web mining, text mining and bioengineering.--

ISBN: 9780470713938 :$90

LCCN: 2019011642Subjects--Topical Terms:

182711
Cluster analysis.

LC Class. No.: QA278.55 / .B55 2020

Dewey Class. No.: 519.5/3

Clustering methodology for symbolic data /
LDR:02334nam a2200289 a 4500 001 571040
003 OCoLC
005 20200807031851.0
008 200908s2020 njua b 001 0 eng
010 $a 2019011642
020 $a 9780470713938 : $c $90
020 $z 9781119010388 (adobe PDF)
020 $z 9781119010395 (ePub)
023 0 2 $a cam a2200277 a 450
035 $a (OCoLC)1097466739
035 $a on1097466739
040 $a DLC $b eng $c DLC $d OCLCF $d OCLCO $d YDX $d FIE
042 $a pcc
050 0 0 $a QA278.55 $b .B55 2020
082 0 0 $a 519.5/3 $2 23
100 1 $a Billard, L. $q (Lynne), $d 1943- $3 435208
245 1 0 $a Clustering methodology for symbolic data / $c Lynne Billard, Edwin Diday.
260 $a Hoboken, NJ : $b Wiley, $c 2020.
300 $a vii, 340 p. : $b ill. ; $c 24 cm.
490 1 $a Wiley series in computational statistics
504 $a Includes bibliographical references and index.
505 0 $a Introduction -- Symbolic data: basics -- Dissimilarity, similarity, and distance measures -- Dissimilarity, similarity, and distance measures: modal data -- General clustering techniques -- Partitioning techniques -- Divisive hierarchical clustering -- Agglomerative hierarchical clustering.
520 $a This book presents all of the latest developments in the field of clustering methodology for symbolic data--paying special attention to the classification methodology for multi-valued list, interval-valued and histogram-valued data methodology, along with numerous worked examples. The book also offers an expansive discussion of data management techniques showing how to manage the large complex dataset into more manageable datasets ready for analyses. Filled with examples, tables, figures, and case studies, Clustering Methodology for Symbolic Data begins by offering chapters on data management, distance measures, general clustering techniques, partitioning, divisive clustering, and agglomerative and pyramid clustering. Clustering Methodology for Symbolic Data will appeal to practitioners of symbolic data analysis, such as statisticians and economists within the public sectors. It will also be of interest to postgraduate students of, and researchers within, web mining, text mining and bioengineering.-- $c Provided by publisher.
650 0 $a Cluster analysis. $3 182711
650 0 $a Multivariate analysis. $3 181905
700 1 $a Diday, E., $e author. $3 857870
776 0 8 $i Online version: $a Billard, L. (Lynne), 1943- author. $t Clustering methodology for symbolic data $d Hoboken, NJ : Wiley, 2019 $z 9781119010388 $w (DLC) 2019018340
830 0 $a Wiley series in computational statistics. $3 541778