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E-raamat: Metaheuristics for Big Data

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  • Formaat: EPUB+DRM
  • Ilmumisaeg: 16-Aug-2016
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119347606
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Metaheuristics for Big DataSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 16-Aug-2016
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119347606
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Big Data is a new field, with many technological challenges to be understood in order to use it to its full potential.  These challenges arise at all stages of working with Big Data, beginning with data generation and acquisition. The storage and management phase presents two critical challenges: infrastructure, for storage and transportation, and conceptual models. Finally, to extract meaning from Big Data requires complex analysis. Here the authors propose using metaheuristics as a solution to these challenges; they are first able to deal with large size problems and secondly flexible and therefore easily adaptable to different types of data and different contexts. The use of metaheuristics to overcome some of these data mining challenges is introduced and justified in the first part of the book, alongside a specific protocol for the performance evaluation of algorithms.  An introduction to metaheuristics follows. The second part of the book details a number of data mining tasks, including clustering, association rules, supervised classification and feature selection, before explaining how metaheuristics can be used to deal with them. This book is designed to be self-contained, so that readers can understand all of the concepts discussed within it, and to provide an overview of recent applications of metaheuristics to knowledge discovery problems in the context of Big Data.
Acknowledgments xi
Introduction xiii
Chapter 1 Optimization and Big Data
1(22)
1.1 Context of Big Data
1(9)
1.1.1 Examples of situations
2(1)
1.1.2 Definitions
3(2)
1.1.3 Big Data challenges
5(3)
1.1.4 Metaheuristics and Big Data
8(2)
1.2 Knowledge discovery in Big Data
10(7)
1.2.1 Data mining versus knowledge discovery
10(2)
1.2.2 Main data mining tasks
12(4)
1.2.3 Data mining tasks as optimization problems
16(1)
1.3 Performance analysis of data mining algorithms
17(4)
1.3.1 Context
17(1)
1.3.2 Evaluation among one or several dataset(s)
18(2)
1.3.3 Repositories and datasets
20(1)
1.4 Conclusion
21(2)
Chapter 2 Metaheuristics -- A Short Introduction
23(30)
2.1 Introduction
24(2)
2.1.1 Combinatorial optimization problems
24(1)
2.1.2 Solving a combinatorial optimization problem
25(1)
2.1.3 Main types of optimization methods
25(1)
2.2 Common concepts of metaheuristics
26(5)
2.2.1 Representation/encoding
27(1)
2.2.2 Constraint satisfaction
28(1)
2.2.3 Optimization criterion/objective function
28(1)
2.2.4 Performance analysis
29(2)
2.3 Single solution-based/local search methods
31(7)
2.3.1 Neighborhood of a solution
31(2)
2.3.2 Hill climbing algorithm
33(1)
2.3.3 Tabu Search
34(1)
2.3.4 Simulated annealing and threshold acceptance approach
35(1)
2.3.5 Combining local search approaches
36(2)
2.4 Population-based metaheuristics
38(5)
2.4.1 Evolutionary computation
38(3)
2.4.2 Swarm intelligence
41(2)
2.5 Multi-objective metaheuristics
43(9)
2.5.1 Basic notions in multi-objective optimization
44(3)
2.5.2 Multi-objective optimization using metaheuristics
47(4)
2.5.3 Performance assessment in multi-objective optimization
51(1)
2.6 Conclusion
52(1)
Chapter 3 Metaheuristics and Parallel Optimization
53(10)
3.1 Parallelism
53(2)
3.1.1 Bit-level
53(1)
3.1.2 Instruction-level parallelism
54(1)
3.1.3 Task and data parallelism
54(1)
3.2 Parallel metaheuristics
55(2)
3.2.1 General concepts
55(1)
3.2.2 Parallel single solution-based metaheuristics
55(2)
3.2.3 Parallel population-based metaheuristics
57(1)
3.3 Infrastructure and technologies for parallel metaheuristics
57(3)
3.3.1 Distributed model
57(1)
3.3.2 Hardware model
58(2)
3.4 Quality measures
60(1)
3.4.1 Speedup
60(1)
3.4.2 Efficiency
61(1)
3.4.3 Serial fraction
61(1)
3.5 Conclusion
61(2)
Chapter 4 Metaheuristics and Clustering
63(24)
4.1 Task description
63(5)
4.1.1 Partitioning methods
65(1)
4.1.2 Hierarchical methods
66(1)
4.1.3 Grid-based methods
67(1)
4.1.4 Density-based methods
67(1)
4.2 Big Data and clustering
68(1)
4.3 Optimization model
68(13)
4.3.1 A combinatorial problem
69(1)
4.3.2 Quality measures
69(7)
4.3.3 Representation
76(5)
4.4 Overview of methods
81(1)
4.5 Validation
82(4)
4.5.1 Internal validation
84(1)
4.5.2 External validation
84(2)
4.6 Conclusion
86(1)
Chapter 5 Metaheuristics and Association Rules
87(22)
5.1 Task description and classical approaches
88(2)
5.1.1 Initial problem
88(1)
5.1.2 A priori algorithm
89(1)
5.2 Optimization model
90(3)
5.2.1 A combinatorial problem
90(1)
5.2.2 Quality measures
90(1)
5.2.3 A mono-or a multi-objective problem?
91(2)
5.3 Overview of metaheuristics for the association rules mining problem
93(12)
5.3.1 Generalities
93(1)
5.3.2 Metaheuristics for categorical association rules
94(5)
5.3.3 Evolutionary algorithms for quantitative association rules
99(3)
5.3.4 Metaheuristics for fuzzy association rules
102(3)
5.4 General table
105(2)
5.5 Conclusion
107(2)
Chapter 6 Metaheuristics and (Supervised) Classification
109(26)
6.1 Task description and standard approaches
110(4)
6.1.1 Problem description
110(1)
6.1.2 K-nearest neighbor
110(1)
6.1.3 Decision trees
111(1)
6.1.4 Naive Bayes
112(1)
6.1.5 Artificial neural networks
113(1)
6.1.6 Support vector machines
114(1)
6.2 Optimization model
114(4)
6.2.1 A combinatorial problem
114(1)
6.2.2 Quality measures
114(3)
6.2.3 Methodology of performance evaluation in supervised classification
117(1)
6.3 Metaheuristics to build standard classifiers
118(8)
6.3.1 Optimization of K-NN
118(1)
6.3.2 Decision tree
119(3)
6.3.3 Optimization of ANN
122(2)
6.3.4 Optimization of SVM
124(2)
6.4 Metaheuristics for classification rules
126(6)
6.4.1 Modeling
126(1)
6.4.2 Objective function(s)
127(2)
6.4.3 Operators
129(1)
6.4.4 Algorithms
130(2)
6.5 Conclusion
132(3)
Chapter 7 On the Use of Metaheuristics for Feature Selection in Classification
135(12)
7.1 Task description
136(2)
7.1.1 Filter models
136(1)
7.1.2 Wrapper models
137(1)
7.1.3 Embedded models
137(1)
7.2 Optimization model
138(5)
7.2.1 A combinatorial optimization problem
138(1)
7.2.2 Representation
139(1)
7.2.3 Operators
140(1)
7.2.4 Quality measures
140(3)
7.2.5 Validation
143(1)
7.3 Overview of methods
143(1)
7.4 Conclusion
144(3)
Chapter 8 Frameworks
147(12)
8.1 Frameworks for designing metaheuristics
147(4)
8.1.1 Easylocal++
148(1)
8.1.2 HeuristicLab
148(1)
8.1.3 jMetal
149(1)
8.1.4 Mallba
149(1)
8.1.5 ParadisEO
150(1)
8.1.6 ECJ
150(1)
8.1.7 OpenBeagle
151(1)
8.1.8 JCLEC
151(1)
8.2 Framework for data mining
151(2)
8.2.1 Orange
152(1)
8.2.2 R and Rattle GUI
153(1)
8.3 Framework for data mining with metaheuristics
153(4)
8.3.1 RapidMiner
154(1)
8.3.2 WEKA
154(1)
8.3.3 KEEL
155(2)
8.3.4 MO-Mine
157(1)
8.4 Conclusion
157(2)
Conclusion 159(2)
Bibliography 161(26)
Index 187
Clarisse DHAENENS is Professor at the University of Lille in France and belongs to a research team working with both CRIStAL Laboratory (UMR CNRS) and Inria. Laetitia JOURDAN is Professor at the University of Lille in France and belongs to a research team working with both CRIStAL Laboratory (UMR CNRS) and Inria.
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