Decision Support System. Tools and Techniques

✍ Scribed by Susmita Bandyopadhyay

• Publisher: CRC Press
• Year: 2023
• Tongue: English
• Leaves: 394
• Category: Library

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
About the Author
Preface
Chapter 1 Introduction
1.1 Introduction
1.2 Classification of Decision Support System
1.3 Decision Support Tools
1.4 Overall Method of Decision-Making
1.5 Brief Introduction to Each Chapter in This Book
Chapter 2 Decision Tree
2.1 Basic Concept
2.2 Algorithms for Construction of Decision Tree
2.2.1 ID3
2.2.1.1 Temperature
2.2.1.2 Humidity
2.2.1.3 Distance
2.2.1.4 Expense
2.2.1.5 Outlook
2.2.1.6 Temperature
2.2.1.7 Humidity
2.2.1.8 Distance
2.2.1.9 Expense
2.2.1.10 Outlook
2.2.2 C4.5
2.3 Time Complexity of Decision Tree
2.4 Various Applications of Decision Tree
2.5 Conclusion
Reference
Chapter 3 Decision Table
3.1 Basic Concept
3.1.1 Limited Entry Decision Table
3.1.2 Extended Entry Decision Table
3.1.3 Mixed Entry Decision Table
3.2 Approaches to Handle Inconsistency for Decision Tables
3.3 Decision Table Languages
3.3.1 Base Language
3.3.2 Rule Selection
3.3.3 Outer Language
3.4 Different Modifications of Decision Table and Latest Trend
3.5 Applications of Different Techniques on Decision Tables
3.6 Conclusion
References
Chapter 4 Predicate Logic
4.1 Introduction
4.2 Latest Research Studies on Predicate and Propositional Logic
4.3 Conclusion
References
Chapter 5 Fuzzy Theory and Fuzzy Logic
5.1 Basic Concepts
5.2 Fuzzification and Defuzzification
5.3 Some Advanced Fuzzy Sets
5.4 Conclusion
References
Chapter 6 Network Tools
6.1 Basic Concepts
6.2 Gantt Chart
6.3 Milestone Chart
6.4 Graphical Evaluation and Review Technique
6.5 Modifications of Traditional Tools
6.6 Conclusion
References
Chapter 7 Petri Net
7.1 Introduction
7.2 Different Types of Petri Nets
7.2.1 Autonomous Petri Net
7.2.2 State Graph
7.2.3 Event Graph
7.2.4 Conflict-Free Petri Net
7.2.5 Free-Choice Petri Net
7.2.6 Simple Petri Net
7.2.7 Pure Petri Net
7.2.8 Generalized Petri Net
7.2.9 Capacitated Petri Nets
7.2.10 Bounded Petri Net
7.2.11 Safe Petri Net
7.2.12 Colored Petri Net
7.2.13 Deadlock
7.3 Continuous and Hybrid Petri Nets
7.4 Basic Modeling Construct of Petri Net
7.5 Modifications of Different Types of Petri Nets and Latest Research Trends
7.6 Conclusion
References
Chapter 8 Markov Chain
8.1 Introduction
8.2 Transition Probability
8.2.1 Calculation of Transition Probability from the Current State
8.2.2 Calculation of Transition Probability from the Current State and the Previous State
8.2.3 Calculation of Multi-Step Transition Probability
8.3 Classification of Markov Chain
8.4 Some Other Miscellaneous Aspects
8.4.1 Canonical Form of Transition Matrix
8.4.2 Steady-State Probabilities for a Regular Markov Chain
8.5 Variations and Modifications of Markov Chains
8.6 Markov Chain Monte Carlo
8.6.1 Gibb's Sampling
8.7 Applications of Markov Chain
8.8 Conclusion
Reference
Chapter 9 Case-Based Reasoning
9.1 Introduction
9.2 Basic Elements and Basic Method
9.2.1 Similarity and Retrieval
9.2.2 CBR Tools
9.2.3 Case Presentation
9.3 Advanced Methods of CBR
9.4 Applications of Case-Based Reasoning and Latest Research
9.5 Conclusion
References
Chapter 10 Multi-Criteria Decision Analysis Techniques
10.1 Basic Concept
10. 2 Benchmark MCDA Techniques
10.2.1 TOPSIS
10.2.2 PROMETHEE
10.2.3 AHP
10.2.4 ANP
10.2.5 MAUT
10.2.6 MACBETH
10.2.7 MOORA
10.2.8 COPRAS
10.2.9 WASPAS
10.2.10 MABAC
10.3 Comparison Among MCDA Techniques
10.3.1 Theoretical Comparison
10.3.2 Rank Correlation Methods
10.3.3 A Newly Proposed Method
10.4 Modification of MCDA Techniques
10.5 Conclusion
References
Chapter 11 Some Other Tools
11.1 Introduction
11.2 Linear Programming
11.2.1 Simplex Method
11.2.1.1 Iteration 1
11.2.1.2 Iteration 2
11.2.1.3 Iteration 3
11.2.2 Two-Phase Method
11.2.2.1 Phase – I
11.2.2.2 First Iteration
11.2.2.3 Iteration 2
11.2.3 Big-M Method
11.2.3.1 First Iteration
11.2.3.2 Second Iteration
11.2.3.3 LPP with Unbounded Solution
11.2.4 Dual Simplex Method
11.2.5 Linear Fractional Programming
11.3 Simulation
11.3.1 Linear Congruential Generator (LCG)
11.3.2 Multiplicative Congruential Generator (MCG)
11.4 Big Data Analytics
11.5 Internet of Things
11.6 Conclusion
References
Chapter 12 Spatial Decision Support System
12.1 Introduction
12.2 Components of SDSS
12.3 SDSS Software
12.4 GRASS GIS Software
12.5 Conclusion
References
Chapter 13 Data Warehousing and Data Mining
13.1 Introduction
13.2 Data Warehouse
13.3 Data Mining
13.3.1 Process of Data Mining
13.3.2 Predictive Modeling
13.3.2.1 Linear Regression
13.3.3 Multiple Linear Regression
13.3.3.1 Assumptions for MLR
13.3.3.2 Linearity
13.3.3.3 Homoscedasticity
13.3.3.4 Uncorrelated Error Terms
13.3.3.5 Estimation of Model Parameters β
13.3.4 Quadratic Trend
13.3.4.1 Logarithmic Trend
13.3.4.2 Association Rules
13.3.4.3 Basic Concept
13.3.4.4 Support and Confidence
13.3.4.5 Association Rule Mining
13.3.4.6 Lift Measure
13.3.4.7 Sequence Rules
13.3.4.8 Segmentation
13.3.4.9 K-Means Clustering
13.3.4.10 Self-Organizing Maps
13.3.4.11 Database Segmentation
13.3.4.12 Clustering for Database Segmentation
13.3.4.13 Cluster Analysis: A Process Model (Figure 13.18)
13.4 Conclusion
References
Chapter 14 Intelligent Decision Support System
14.1 Introduction
14.2 Enterprise Information System
14.3 Knowledge Management
14.3.1 Concept Map
14.3.2 Semantic Network
14.4 Artificial Intelligence
14.4.1 Propositional Logic
14.4.2 Nature–Based Optimization Techniques
14.4.2.1 Genetic Algorithm
14.4.2.2 Particle Swarm Optimization
14.4.2.3 Ant Colony Optimization (ACO)
14.4.2.4 Artificial Immune Algorithm (AIA)
14.4.2.5 Differential Evolution (DE)
14.4.2.6 Simulated Annealing
14.4.2.7 Tabu Search
14.4.2.8 Gene Expression Programming
14.4.2.9 Frog Leaping Algorithm
14.4.2.10 Honey Bee Mating Algorithm (HBMA)
14.4.2.11 Bacteria Foraging Algorithm (BFA)
14.4.2.12 Cultural Algorithm (CA)
14.4.2.13 Firefly Algorithm (FA)
14.4.2.14 Cuckoo Search (CS)
14.4.2.15 Gravitational Search Algorithm (GSA)
14.4.2.16 Charged System Search
14.4.2.17 Intelligent Water Drops Algorithm
14.4.2.18 Bat Algorithm (BA)
14.4.2.19 Black Hole Algorithm (BHA)
14.4.2.20 Black Widow Optimization (BWO) Algorithm
14.4.2.21 Butterfly Optimization Algorithm (BOA)
14.4.2.22 Crow Search Algorithm (CSA)
14.4.2.23 Deer Hunting Optimization (DHO) Algorithm
14.4.2.24 Dragonfly Algorithm (DA)
14.4.2.25 Emperor Penguin Optimization (EPO)
14.4.2.26 Flower Pollination Algorithm (FPA)
14.4.2.27 Glowworm Swarm Based Optimization
14.4.2.28 Grasshopper Optimization Algorithm (GOA)
14.4.2.29 Grey Wolf Optimization (GWO)
14.4.2.30 Krill Herd Algorithm (KHA)
14.4.2.31 Lion Optimization Algorithm
14.4.2.32 Migratory Birds Optimization (MBO)
14.4.2.33 Moth-Flame Optimization Algorithm
14.4.2.34 Mouth-Brooding Fish Algorithm
14.4.2.35 Polar Bear Optimization Algorithm
14.4.2.36 Whale Optimization Algorithm (WOA)
14.4.2.37 Sea Lion Optimization Algorithm (SLOA)
14.4.2.38 Tarantula Mating-Based Strategy (TMS)
14.4.3 Some Latest Tools for Recent Applications of Artificial Intelligence
14.4.3.1 Cloud Computing
14.4.3.2 Big Data
14.5 Conclusion
References
Chapter 15 DSS Software
15.1 Introduction
15.2 Software Overview for DT
15.2.1 KNIME
15.3 Software Overview for Networking Techniques
15.4 Software Overview for Markov Process and Markov Chain
15.5 Software Overview for Regression
15.6 Software Overview for LP
15.7 Software Overview for Simulation
15.7.1 Create
15.7.2 Process
15.7.3 Decide
15.7.4 Dispose
15.7.5 Assign
15.8 Software Overview for Data Warehouse
15.9 Software Overview for Other Common Software
15.9.1 Matlab
15.9.2 C#.net
15.10 Conclusion
Reference
Chapter 16 Future of Decision Support System
16.1 Introduction
16.2 Conclusion
References
Index