東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ subject:"Data Analysis and Big Data." ]

切換: 標籤 | MARC模式 | ISBD

Complex data analytics with formal c...

Missaoui, R.

FindBook

Google Book

Amazon

博客來

Complex data analytics with formal concept analysis

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Complex data analytics with formal concept analysis/ edited by Rokia Missaoui, Leonard Kwuida, Talel Abdessalem.
其他作者:	Missaoui, R.
出版者:	Cham :Springer International Publishing : : 2022.,
面頁冊數:	xxv, 260 p. :ill., digital ;24 cm.
內容註:	Chapter. 1 -- Formal Concept Analysis and Extensions for Complex Data Analytics -- Chapter. 2 -- Conceptual Navigation in Large Knowledge Graphs -- Chapter. 3 -- FCA2VEC: Embedding Techniques for Formal Concept Analysis -- Chapter. 4 -- Analysis of Complex and Heterogeneous Data using FCA and Monadic Predicates -- Chapter. 5 -- Dealing with Large Volumes of Complex Relational Data using RCA -- Chapter. 6 -- Computing Dependencies using FCA -- Chapter. 7 -- Leveraging Closed Patterns and Formal Concept Analysis for Enhanced Microblogs Retrieval -- Chapter. 8 -- Scalable Visual Analytics in FCA -- Chapter. 9 -- Formal methods in FCA and Big Data -- Chapter. 10 -- Towards Distributivity in FCA for Phylogenetic Data -- Chapter. 11 -- Triclustering in Big Data Setting.
Contained By:	Springer Nature eBook
標題:	Data mining. -
電子資源:	https://doi.org/10.1007/978-3-030-93278-7
ISBN:	9783030932787

Complex data analytics with formal concept analysis
Complex data analytics with formal concept analysis[electronic resource] /edited by Rokia Missaoui, Leonard Kwuida, Talel Abdessalem. - Cham :Springer International Publishing :2022. - xxv, 260 p. :ill., digital ;24 cm.

Chapter. 1 -- Formal Concept Analysis and Extensions for Complex Data Analytics -- Chapter. 2 -- Conceptual Navigation in Large Knowledge Graphs -- Chapter. 3 -- FCA2VEC: Embedding Techniques for Formal Concept Analysis -- Chapter. 4 -- Analysis of Complex and Heterogeneous Data using FCA and Monadic Predicates -- Chapter. 5 -- Dealing with Large Volumes of Complex Relational Data using RCA -- Chapter. 6 -- Computing Dependencies using FCA -- Chapter. 7 -- Leveraging Closed Patterns and Formal Concept Analysis for Enhanced Microblogs Retrieval -- Chapter. 8 -- Scalable Visual Analytics in FCA -- Chapter. 9 -- Formal methods in FCA and Big Data -- Chapter. 10 -- Towards Distributivity in FCA for Phylogenetic Data -- Chapter. 11 -- Triclustering in Big Data Setting.

FCA is an important formalism that is associated with a variety of research areas such as lattice theory, knowledge representation, data mining, machine learning, and semantic Web. It is successfully exploited in an increasing number of application domains such as software engineering, information retrieval, social network analysis, and bioinformatics. Its mathematical power comes from its concept lattice formalization in which each element in the lattice captures a formal concept while the whole structure represents a conceptual hierarchy that offers browsing, clustering and association rule mining. Complex data analytics refers to advanced methods and tools for mining and analyzing data with complex structures such as XML/Json data, text and image data, multidimensional data, graphs, sequences and streaming data. It also covers visualization mechanisms used to highlight the discovered knowledge. This edited book examines a set of important and relevant research directions in complex data management, and updates the contribution of the FCA community in analyzing complex and large data such as knowledge graphs and interlinked contexts. For example, Formal Concept Analysis and some of its extensions are exploited, revisited and coupled with recent processing parallel and distributed paradigms to maximize the benefits in analyzing large data.

ISBN: 9783030932787

Standard No.: 10.1007/978-3-030-93278-7doiSubjects--Topical Terms:

562972
Data mining.

LC Class. No.: QA76.9.D343 / C65 2022

Dewey Class. No.: 006.312

Complex data analytics with formal concept analysis
LDR:03202nmm a2200337 a 4500 001 2301827
003 DE-He213
005 20220629150427.0
006 m d
007 cr nn 008maaau
008 230409s2022 sz s 0 eng d
020 $a 9783030932787 $q (electronic bk.)
020 $a 9783030932770 $q (paper)
024 7 $a 10.1007/978-3-030-93278-7 $2 doi
035 $a 978-3-030-93278-7
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.D343 $b C65 2022
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.312 $2 23
090 $a QA76.9.D343 $b C737 2022
245 0 0 $a Complex data analytics with formal concept analysis $h [electronic resource] / $c edited by Rokia Missaoui, Leonard Kwuida, Talel Abdessalem.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a xxv, 260 p. : $b ill., digital ; $c 24 cm.
338 $a online resource $b cr $2 rdacarrier
505 0 $a Chapter. 1 -- Formal Concept Analysis and Extensions for Complex Data Analytics -- Chapter. 2 -- Conceptual Navigation in Large Knowledge Graphs -- Chapter. 3 -- FCA2VEC: Embedding Techniques for Formal Concept Analysis -- Chapter. 4 -- Analysis of Complex and Heterogeneous Data using FCA and Monadic Predicates -- Chapter. 5 -- Dealing with Large Volumes of Complex Relational Data using RCA -- Chapter. 6 -- Computing Dependencies using FCA -- Chapter. 7 -- Leveraging Closed Patterns and Formal Concept Analysis for Enhanced Microblogs Retrieval -- Chapter. 8 -- Scalable Visual Analytics in FCA -- Chapter. 9 -- Formal methods in FCA and Big Data -- Chapter. 10 -- Towards Distributivity in FCA for Phylogenetic Data -- Chapter. 11 -- Triclustering in Big Data Setting.
520 $a FCA is an important formalism that is associated with a variety of research areas such as lattice theory, knowledge representation, data mining, machine learning, and semantic Web. It is successfully exploited in an increasing number of application domains such as software engineering, information retrieval, social network analysis, and bioinformatics. Its mathematical power comes from its concept lattice formalization in which each element in the lattice captures a formal concept while the whole structure represents a conceptual hierarchy that offers browsing, clustering and association rule mining. Complex data analytics refers to advanced methods and tools for mining and analyzing data with complex structures such as XML/Json data, text and image data, multidimensional data, graphs, sequences and streaming data. It also covers visualization mechanisms used to highlight the discovered knowledge. This edited book examines a set of important and relevant research directions in complex data management, and updates the contribution of the FCA community in analyzing complex and large data such as knowledge graphs and interlinked contexts. For example, Formal Concept Analysis and some of its extensions are exploited, revisited and coupled with recent processing parallel and distributed paradigms to maximize the benefits in analyzing large data.
650 0 $a Data mining. $3 562972
650 0 $a Big data. $3 2045508
650 0 $a Formal methods (Computer science) $3 882407
650 1 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Data Analysis and Big Data. $3 3538537
700 1 $a Missaoui, R. $3 3601583
700 1 $a Kwuida, Leonard. $3 1085709
700 1 $a Abdessalem, Talel. $3 3236012
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-030-93278-7
950 $a Computer Science (SpringerNature-11645)