東華大學圖書館 |

Data mining and big data = 6th International Conference, DMBD 2021, Guangzhou, China, October 20-22, 2021 : proceedings.. Part I /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data mining and big data/ edited by Ying Tan ... [et al.].
其他題名:	6th International Conference, DMBD 2021, Guangzhou, China, October 20-22, 2021 : proceedings.
其他題名:	DMBD 2021
其他作者:	Tan, Ying,
團體作者:	International Conference on Data Mining and Big Data
出版者:	Singapore :Springer Singapore : : 2021.,
面頁冊數:	xviii, 501 p. :ill., digital ;24 cm.
內容註:	Intro -- 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 2 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 4.4 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 Application 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" 3.2 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.
Contained By:	Springer Nature eBook
標題:	Data mining - Congresses. -
電子資源:	https://doi.org/10.1007/978-981-16-7476-1
ISBN:	9789811674761

Data mining and big data = 6th International Conference, DMBD 2021, Guangzhou, China, October 20-22, 2021 : proceedings.. Part I /
Data mining and big data6th International Conference, DMBD 2021, Guangzhou, China, October 20-22, 2021 : proceedings.Part I /[electronic resource] :DMBD 2021edited by Ying Tan ... [et al.]. - Singapore :Springer Singapore :2021. - xviii, 501 p. :ill., digital ;24 cm. - Communications in computer and information science,14531865-0937 ;. - Communications in computer and information science ;1453..

Intro -- 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 2 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 4.4 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 Application 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" 3.2 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.

This 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.

ISBN: 9789811674761

Standard No.: 10.1007/978-981-16-7476-1doiSubjects--Topical Terms:

551626
Data mining
--Congresses.

LC Class. No.: QA76.9.D343 / I58 2021

Dewey Class. No.: 006.312

Data mining and big data = 6th International Conference, DMBD 2021, Guangzhou, China, October 20-22, 2021 : proceedings.. Part I /
LDR:04684nmm a2200349 a 4500 001 2253512
003 DE-He213
005 20211030160150.0
006 m d
007 cr nn 008maaau
008 220327s2021 si s 0 eng d
020 $a 9789811674761 $q (electronic bk.)
020 $a 9789811674754 $q (paper)
024 7 $a 10.1007/978-981-16-7476-1 $2 doi
035 $a 978-981-16-7476-1
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.D343 $b I58 2021
072 7 $a UB $2 bicssc
072 7 $a COM018000 $2 bisacsh
072 7 $a UB $2 thema
082 0 4 $a 006.312 $2 23
090 $a QA76.9.D343 $b I61 2021
111 2 $a International Conference on Data Mining and Big Data $n (6th: $d 2021 : $c Guangzhou, China) $3 3521736
245 1 0 $a Data mining and big data $h [electronic resource] : $b 6th International Conference, DMBD 2021, Guangzhou, China, October 20-22, 2021 : proceedings. $n Part I / $c edited by Ying Tan ... [et al.].
246 3 $a DMBD 2021
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2021.
300 $a xviii, 501 p. : $b ill., digital ; $c 24 cm.
490 1 $a Communications in computer and information science, $x 1865-0937 ; $v 1453
505 0 $a Intro -- 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 2 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 4.4 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 Application 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" 3.2 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.
520 $a This 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.
650 0 $a Data mining $v Congresses. $3 551626
650 0 $a Big data $v Congresses. $3 3166510
650 1 4 $a Computer Applications. $3 891249
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Computer Communication Networks. $3 775497
650 2 4 $a Computer Imaging, Vision, Pattern Recognition and Graphics. $3 890871
700 1 $a Tan, Ying, $d 1964- $3 3521737
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Communications in computer and information science ; $v 1453. $3 3521748
856 4 0 $u https://doi.org/10.1007/978-981-16-7476-1
950 $a Computer Science (SpringerNature-11645)