LEADER 04503nam 22006135 450 001 9910337839003321 005 20200705041406.0 010 $a3-030-02985-9 024 7 $a10.1007/978-3-030-02985-2 035 $a(CKB)4100000008047983 035 $a(MiAaPQ)EBC5771184 035 $a(DE-He213)978-3-030-02985-2 035 $a(PPN)235668990 035 $a(EXLCZ)994100000008047983 100 $a20190430d2019 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdaptive Resonance Theory in Social Media Data Clustering $eRoles, Methodologies, and Applications /$fby Lei Meng, Ah-Hwee Tan, Donald C. Wunsch II 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (200 pages) 225 1 $aAdvanced Information and Knowledge Processing,$x1610-3947 311 $a3-030-02984-0 327 $aPart 1: Theories -- Introduction -- Clustering and Extensions in the Social Media Domain -- Adaptive Resonance Theory (ART) for Social Media Analytics -- Part II: Applications -- Personalized Web Image Organization -- Socially-Enriched Multimedia Data Co-Clustering -- Community Discovery in Heterogeneous Social Networks -- Online Multimodal Co-Indexing and Retrieval of Social Media Data -- Concluding Remarks. 330 $aSocial media data contains our communication and online sharing, mirroring our daily life. This book looks at how we can use and what we can discover from such big data: Basic knowledge (data & challenges) on social media analytics Clustering as a fundamental technique for unsupervised knowledge discovery and data mining A class of neural inspired algorithms, based on adaptive resonance theory (ART), tackling challenges in big social media data clustering Step-by-step practices of developing unsupervised machine learning algorithms for real-world applications in social media domain Adaptive Resonance Theory in Social Media Data Clustering stands on the fundamental breakthrough in cognitive and neural theory, i.e. adaptive resonance theory, which simulates how a brain processes information to perform memory, learning, recognition, and prediction. It presents initiatives on the mathematical demonstration of ART?s learning mechanisms in clustering, and illustrates how to extend the base ART model to handle the complexity and characteristics of social media data and perform associative analytical tasks. Both cutting-edge research and real-world practices on machine learning and social media analytics are included in the book and if you wish to learn the answers to the following questions, this book is for you: How to process big streams of multimedia data? How to analyze social networks with heterogeneous data? How to understand a user?s interests by learning from online posts and behaviors? How to create a personalized search engine by automatically indexing and searching multimodal information resources? 410 0$aAdvanced Information and Knowledge Processing,$x1610-3947 606 $aData mining 606 $aAlgorithms 606 $aCognitive psychology 606 $aPattern perception 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 606 $aCognitive Psychology$3https://scigraph.springernature.com/ontologies/product-market-codes/Y20060 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 615 0$aData mining. 615 0$aAlgorithms. 615 0$aCognitive psychology. 615 0$aPattern perception. 615 14$aData Mining and Knowledge Discovery. 615 24$aAlgorithm Analysis and Problem Complexity. 615 24$aCognitive Psychology. 615 24$aPattern Recognition. 676 $a005.7 676 $a005.7 700 $aMeng$b Lei$4aut$4http://id.loc.gov/vocabulary/relators/aut$01057907 702 $aTan$b Ah-Hwee$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aWunsch II$b Donald C$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910337839003321 996 $aAdaptive Resonance Theory in Social Media Data Clustering$92495416 997 $aUNINA