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Conference PAKDD (Conference) (22nd : 2018 : Melbourne, Vic.)
Title Advances in knowledge discovery and data mining : 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings. Part III / Dinh Phung, Vincent S. Tseng, Geoffrey I. Webb, Bao Ho, Mohadeseh Ganji, Lida Rashidi (eds.).
Imprint Cham, Switzerland : Springer, 2018.

LOCATION CALL # STATUS MESSAGE
 OHIOLINK SPRINGER EBOOKS    ONLINE  
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Conference PAKDD (Conference) (22nd : 2018 : Melbourne, Vic.)
Series Lecture notes in computer science, 0302-9743 ; 10939
Lecture notes in artificial intelligence
LNCS sublibrary. SL 7, Artificial intelligence
Lecture notes in computer science ; 10939. 0302-9743
Lecture notes in computer science. Lecture notes in artificial intelligence.
LNCS sublibrary. SL 7, Artificial intelligence.
Subject Data mining -- Congresses.
Alt Name Phung, Dinh,
Tseng, Vincent S.,
Webb, Geoffrey I.,
Ho, Bao,
Ganji, Mohadeseh,
Rashidi, Lida,
Add Title PAKDD 2018
LOCATION CALL # STATUS MESSAGE
 OHIOLINK SPRINGER EBOOKS    ONLINE  
View online
Conference PAKDD (Conference) (22nd : 2018 : Melbourne, Vic.)
Series Lecture notes in computer science, 0302-9743 ; 10939
Lecture notes in artificial intelligence
LNCS sublibrary. SL 7, Artificial intelligence
Lecture notes in computer science ; 10939. 0302-9743
Lecture notes in computer science. Lecture notes in artificial intelligence.
LNCS sublibrary. SL 7, Artificial intelligence.
Subject Data mining -- Congresses.
Alt Name Phung, Dinh,
Tseng, Vincent S.,
Webb, Geoffrey I.,
Ho, Bao,
Ganji, Mohadeseh,
Rashidi, Lida,
Add Title PAKDD 2018
Description 1 online resource (xxviii, 835 pages) : illustrations
Note Includes author index.
Online resource; title from PDF title page (SpringerLink, viewed June 20, 2018).
Summary This three-volume set, LNAI 10937, 10938, and 10939, constitutes the thoroughly refereed proceedings of the 22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018, held in Melbourne, VIC, Australia, in June 2018. The 164 full papers were carefully reviewed and selected from 592 submissions. The volumes present papers focusing on new ideas, original research results and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems and the emerging applications.
Contents Intro -- PC Chairs' Preface -- General Chairs' Preface -- Organization -- Contents -- Part III -- Feature Learning and Data Mining Process -- Discovering High Utility Itemsets Based on the Artificial Bee Colony Algorithm -- Abstract -- 1 Introduction -- 2 Preliminaries -- 2.1 Problem of HUI Mining -- 2.2 ABC Algorithm -- 3 Mining HUIs Using the ABC -- 3.1 Bitmap Item Information Representation -- 3.2 Modeling HUI Discovery Using the ABC -- 3.3 Direct Nectar Source Generation for Scout Bees -- 3.4 Algorithm Description -- 4 Performance Evaluation -- 4.1 Experimental Environment and Datasets
4.2 Running Time -- 4.3 Number of Discovered HUIs -- 4.4 Convergence -- 5 Conclusions -- Acknowledgments -- References -- A Scalable and Efficient Subgroup Blocking Scheme for Multidatabase Record Linkage -- 1 Introduction -- 2 Related Work -- 3 Subgroup Blocking Process -- 3.1 Potential Candidate Grouping -- 3.2 Candidate Graph Construction -- 3.3 Subgroup Candidate Generation -- 4 Analysis of Subgroup Blocking -- 5 Experiments and Discussion -- 6 Conclusions and Future Work -- References -- Efficient Feature Selection Framework for Digital Marketing Applications -- 1 Introduction
2 Related Work -- 3 Overall Framework -- 4 Feature Exploration Using Semantic Ranking and Generative Filtering -- 4.1 The Semantic Ranking Guided Feature Selection Algorithm -- 4.2 Combining with Generative Filtering for Better Performance -- 5 Progressive Sampling and Feature Selection Framework -- 5.1 Coarse to Fine Implementation -- 5.2 Time and Space Reduction Through Progressive Sampling -- 6 Experiments and Discussion -- 7 Conclusion -- References -- Dynamic Feature Selection Algorithm Based on Minimum Vertex Cover of Hypergraph -- 1 Introduction -- 2 Preliminaries
3 Dynamic Feature Selection Algorithm Based on Minimum Vertex Cover of Hypergraph -- 3.1 The Induced Hypergraph -- 3.2 Updating Minimum Vertex Cover of Hypergraph -- 3.3 Dynamic Feature Selection Algorithm -- 4 Experimental Analysis -- 5 Conclusions -- References -- Feature Selection for Multiclass Binary Data -- 1 Introduction -- 2 Related Work -- 3 Preliminary Concepts -- 4 Problem Formulation -- 5 Our Approach -- 5.1 Measuring the Sparse Value Distribution -- 5.2 New Feature Selection Objective -- 5.3 A Greedy Feature Selection Approach -- 6 Evaluation -- 6.1 Experimental Results
6.2 Evaluation Insights -- 7 Conclusion -- References -- Scalable Model-Based Cascaded Imputation of Missing Data -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 4 Methodology -- 5 Discussion of Experimental Results -- 6 Conclusions -- References -- On Reducing Dimensionality of Labeled Data Efficiently -- 1 Introduction -- 2 Related Work -- 2.1 Metric Learning -- 2.2 Nonlinear Algorithms for Collapsing Classes -- 2.3 Parametric Embedding -- 3 Nonlinear Parametric Embedding -- 4 Evaluation -- 4.1 Experiment Settings -- 4.2 Results -- 5 Conclusion -- References
ISBN 9783319930404 (electronic bk.)
3319930400 (electronic bk.)
9783319930374
3319930370
9783319930411 (print)
9783319930398 (print)
3319930419
3319930397
3319930338
9783319930336
3319930362
9783319930367
ISBN/ISSN 10.1007/978-3-319-93040-4
10.1007/978-3-319-93037-4
OCLC # 1041139274
Additional Format Printed edition: 9783319930398


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