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001440813 0247_ $$a10.1007/978-981-16-7476-1$$2doi
001440813 035__ $$aSP(OCoLC)1283856681
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001440813 049__ $$aISEA
001440813 050_4 $$aQA76.9.D343$$bI58 2021
001440813 08204 $$a006.3/12$$223
001440813 1112_ $$aInternational Conference on Data Mining and Big Data$$n(6th :$$d2021 :$$cGuangzhou, China)
001440813 24510 $$aData mining and big data :$$b6th international conference, DMBD 2021, Guangzhou, China, October 20-22, 2021 : proceedings.$$nPart I /$$cYing Tan, Yuhui Shi, Albert Zomaya, Hongyang Yan, Jun Cai (eds.).
001440813 24630 $$aDMBD 2021
001440813 264_1 $$aSingapore :$$bSpringer,$$c[2021]
001440813 264_4 $$c©2021
001440813 300__ $$a1 online resource (518 pages) :$$billustrations
001440813 336__ $$atext$$btxt$$2rdacontent
001440813 337__ $$acomputer$$bc$$2rdamedia
001440813 338__ $$aonline resource$$bcr$$2rdacarrier
001440813 347__ $$atext file
001440813 347__ $$bPDF
001440813 4901_ $$aCommunications in computer and information science ;$$v1453
001440813 500__ $$aInternational conference proceedings.
001440813 500__ $$aIncludes author index.
001440813 5050_ $$aIntro -- Preface -- Organization -- Contents -- Part I -- Contents -- Part II -- BSMRL: Bribery Selfish Mining with Reinforcement Learning -- 1 Introduction -- 1.1 Related Work -- 2 Preliminaries -- 2.1 Selfish Mining -- 2.2 Bribery Attack -- 2.3 Reinforcement Learning -- 3 Modeling BSMRL -- 3.1 Constructing the Environment -- 3.2 The Attacker's Mining Strategy -- 4 Simulation -- 5 Conclusion and Future Work -- References -- The Theoretical Analysis of Multi-dividing Ontology Learning by Rademacher Vector -- 1 Introduction
001440813 5058_ $$a2 Ontology Learning Framework in Multi-dividing Setting and Prerequisite Knowledge -- 3 Main Result and Proof -- 4 Conclusion -- References -- A Group Blind Signature Scheme for Privacy Protection of Power Big Data in Smart Grid -- Abstract -- 1 Introduction -- 2 Preliminaries -- 2.1 Group Blind Signature -- 2.2 Schnorr Identification Protocol -- 3 System Model and Adversary Model -- 3.1 System Model -- 3.2 Adversary Model -- 4 Our Scheme -- 4.1 System Initialization -- 4.2 User Anonymous Authentication and Data Reporting -- 4.3 Blindly Signature on the Message
001440813 5058_ $$a4.4 Verification and Traceability -- 5 Security Analysis -- 5.1 Authenticatability -- 5.2 Privacy Protection -- 5.3 Anonymity -- 5.4 Unforgeability -- 5.5 Traceability -- 6 Conclusion -- References -- MB Based Multi-dividing Ontology Learning Trick -- 1 Introduction -- 2 MB Based Multi-dividing Ontology Learning Algorithm -- 3 Experiments -- 3.1 Experiment on Mathematics-Physics Disciplines -- 3.2 Ontology Mapping on Sponge City Rainwater Treatment System Ontologies -- 3.3 Experiment on Chemical Index Ontology -- 4 Conclusion -- References
001440813 5058_ $$aApplication of LSTM Model Optimized Based on Adaptive Genetic Algorithm in Stock Forecasting -- Abstract -- 1 Introduction -- 2 Algorithm Background -- 3 Problem Description -- 4 Algorithm Description -- 4.1 Genes Code -- 4.2 Crossover Operator -- 4.3 Mutation Operator -- 4.4 Steps of the Algorithm -- 5 Experimental Result -- 6 Conclusion -- Acknowledgement -- References -- A Network Based Quantitative Method for the Mining and Visualization of Music Influence -- Abstract -- 1 Introduction -- 2 Notations -- 3 LMIFNC Model for Influencer-Follower Network -- 3.1 Features of "Music Influence."
001440813 5058_ $$a3.2 The Influence of Artist -- 3.2.1 The Initial Influence of Artist Drawn from Linkage -- 3.2.2 Logarithm Function for Time-Offset Correction Coefficient C -- 3.2.3 Assigning Weight to the Edges of Influencer-Follower Network -- 3.3 Deriving Influencer-Follower Network and Subnetwork -- 3.3.1 Definition of Modularity and Increment of Modularity -- 3.3.2 Louvain Method -- 3.3.3 Process of Proposed LMIFNC for Influencer-Follower Network Construction -- 4 Experimental Results and Discussion -- 4.1 Data Set -- 4.2 Results and Visualization -- 5 Conclusion and Future Work -- References
001440813 506__ $$aAccess limited to authorized users.
001440813 520__ $$aThis two-volume set, CCIS 1453 and CCIS 1454, constitutes refereed proceedings of the 6th International Conference on Data Mining and Big Data, DMBD 2021, held in Guangzhou, China, in October 2021. The 57 full papers and 28 short papers presented in this two-volume set were carefully reviewed and selected from 258 submissions. The papers present the latest research on advantages in theories, technologies, and applications in data mining and big data. The volume covers many aspects of data mining and big data as well as intelligent computing methods applied to all fields of computer science, machine learning, data mining and knowledge discovery, data science, etc.
001440813 588__ $$aDescription based on print version record.
001440813 650_0 $$aData mining$$vCongresses.
001440813 650_0 $$aBig data$$vCongresses.
001440813 650_6 $$aExploration de données (Informatique)$$vCongrès.
001440813 650_6 $$aDonnées volumineuses$$vCongrès.
001440813 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001440813 655_7 $$aConference papers and proceedings.$$2lcgft
001440813 655_7 $$aActes de congrès.$$2rvmgf
001440813 655_0 $$aElectronic books.
001440813 7001_ $$aTan, Ying,$$d1964-$$eeditor.
001440813 7001_ $$aShi, Yuhui,$$eeditor.
001440813 7001_ $$aZomaya, Albert Y.,$$eeditor.
001440813 7001_ $$aYan, Hongyang,$$eeditor.
001440813 7001_ $$aCai, Jun,$$eeditor.
001440813 77608 $$iPrint version:$$aTan, Ying.$$tData Mining and Big Data.$$dSingapore : Springer Singapore Pte. Limited, ©2021$$z9789811674754
001440813 830_0 $$aCommunications in computer and information science ;$$v1453.
001440813 852__ $$bebk
001440813 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-16-7476-1$$zOnline Access$$91397441.1
001440813 909CO $$ooai:library.usi.edu:1440813$$pGLOBAL_SET
001440813 980__ $$aBIB
001440813 980__ $$aEBOOK
001440813 982__ $$aEbook
001440813 983__ $$aOnline
001440813 994__ $$a92$$bISE