LEADER 03974nam 22005655 450 001 9910735392703321 005 20251113210923.0 010 $a3-030-93278-8 024 7 $a10.1007/978-3-030-93278-7 035 $a(MiAaPQ)EBC7025318 035 $a(Au-PeEL)EBL7025318 035 $a(CKB)24100752800041 035 $a(PPN)269151621 035 $a(OCoLC)1334889768 035 $a(DE-He213)978-3-030-93278-7 035 $a(EXLCZ)9924100752800041 100 $a20220629d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComplex Data Analytics with Formal Concept Analysis /$fedited by Rokia Missaoui, Léonard Kwuida, Talel Abdessalem 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (277 pages) 225 1 $aComputer Science Series 311 08$aPrint version: Missaoui, Rokia Complex Data Analytics with Formal Concept Analysis Cham : Springer International Publishing AG,c2022 9783030932770 327 $aChapter. 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. 330 $aFCA is an important formalism that is associated with a variety of research areas such as lattice theory, knowledge representation, data mining, machine learning, and semantic Web. It is successfully exploited in an increasing number of application domains such as software engineering, information retrieval, social network analysis, and bioinformatics. Its mathematical power comes from its concept lattice formalization in which each element in the lattice captures a formal concept while the whole structure represents a conceptual hierarchy that offers browsing, clustering and association rule mining. Complex data analytics refers to advanced methods and tools for mining and analyzing data with complex structures such as XML/Json data, text and image data, multidimensional data, graphs, sequences and streaming data. It also covers visualization mechanisms used to highlight the discovered knowledge. This edited book examines a set of important and relevant research directions in complex data management, and updates the contribution of the FCA community in analyzing complex and large data such as knowledge graphs and interlinked contexts. For example, Formal Concept Analysis and some of its extensions are exploited, revisited and coupled with recent processing parallel and distributed paradigms to maximize the benefits in analyzing large data. 410 0$aComputer Science Series 606 $aArtificial intelligence 606 $aQuantitative research 606 $aArtificial Intelligence 606 $aData Analysis and Big Data 615 0$aArtificial intelligence. 615 0$aQuantitative research. 615 14$aArtificial Intelligence. 615 24$aData Analysis and Big Data. 676 $a004.0151 676 $a006.312 702 $aKwuida$b Leonard 702 $aAbdessalem$b Talel 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910735392703321 996 $aComplex data analytics with formal concept analysis$92997193 997 $aUNINA