Complex Data Analytics with Formal Concept Analysis (Record no. 176927)

MARC details
000 -LEADER
fixed length control field 04078nam a22005295i 4500
001 - CONTROL NUMBER
control field 978-3-030-93278-7
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240423125337.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr nn 008mamaa
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
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020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783030932787
-- 978-3-030-93278-7
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-3-030-93278-7
Source of number or code doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number Q334-342
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number TA347.A78
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYQ
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM004000
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYQ
Source thema
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.3
Edition number 23
245 10 - TITLE STATEMENT
Title Complex Data Analytics with Formal Concept Analysis
Medium [electronic resource] /
Statement of responsibility, etc edited by Rokia Missaoui, Léonard Kwuida, Talel Abdessalem.
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2022.
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2022.
300 ## - PHYSICAL DESCRIPTION
Extent XXV, 260 p. 87 illus., 27 illus. in color.
Other physical details online resource.
336 ## -
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-- online resource
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505 0# - FORMATTED CONTENTS NOTE
Formatted contents note 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 ## - SUMMARY, ETC.
Summary, etc 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 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Artificial intelligence.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Quantitative research.
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Artificial Intelligence.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data Analysis and Big Data.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Missaoui, Rokia.
Relator term editor.
Relator code edt
-- http://id.loc.gov/vocabulary/relators/edt
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Kwuida, Léonard.
Relator term editor.
Relator code edt
-- http://id.loc.gov/vocabulary/relators/edt
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Abdessalem, Talel.
Relator term editor.
Relator code edt
-- http://id.loc.gov/vocabulary/relators/edt
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer Nature eBook
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783030932770
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783030932794
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783030932800
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://doi.org/10.1007/978-3-030-93278-7">https://doi.org/10.1007/978-3-030-93278-7</a>
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942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks-CSE-Springer

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