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Complex Data Analytics with Formal Concept Analysis / edited by Rokia Missaoui, Léonard Kwuida, Talel Abdessalem

データ種別	電子ブック
版	1st ed. 2022.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2022
大きさ	XXV, 260 p. 87 illus., 27 illus. in color : online resource
著者標目	Missaoui, Rokia editor Kwuida, Léonard editor Abdessalem, Talel editor SpringerLink (Online service)

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URL	巻　次	配架場所	請求記号	登録番号	コメント	刷　年	状　態	利用注記	ISBN	予約	請求メモ
URL		射水-電子	007	EB0002184	Computer Scinece R0 2005-6,2022-3				9783030932787

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一般注記	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 HTTP:URL=https://doi.org/10.1007/978-3-030-93278-7
件　名	LCSH:Artificial intelligence LCSH:Quantitative research FREE:Artificial Intelligence FREE:Data Analysis and Big Data
分　類	LCC:Q334-342 LCC:TA347.A78 DC23:006.3
書誌ID	EB00001572
ISBN	9783030932787