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偏好空间并置模式挖掘（英）
作者：王丽珍 著
出版社：科学出版社
出版时间：2022-01-01
ISBN：9787030713728
定价：¥198.00
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内容简介
《偏好空间同位模式挖掘（英）》以应用需求（领域驱动）为导向，系统介绍了《偏好空间同位模式挖掘（英）》作者多年在领域驱动空间模式挖掘技术方面的研究成果。具体包括不需要距离阈值的空间co-location模式挖掘技术、极大频繁空间co-location模式挖掘技术、极大亚频繁空间co-location模式挖掘技术、SPI-闭频繁co-location模式挖掘技术、非冗余co-location模式挖掘技术、高效用co-location模式挖掘技术、实例带效用的高效用co-location模式挖掘技术、带主导特征的频繁co-location模式挖掘技术和基于概率模型的交互式二次挖掘用户感兴趣的co-location模式挖掘技术等。
作者简介
暂缺《偏好空间并置模式挖掘（英）》作者简介
目录
Contents
1 Introduction 1
1.1 The Background and Applications 1
1.2 The Evolution and Development 5
1.3 The Challenges and Issues 7
1.4 Content and Organization of the Book 8
2 Maximal Prevalent Co-location Patterns 11
2.1 Introduction 11
2.2 Why the MCHT Method Is Proposed for Mining MPCPs 12
2.3 Formal Problem Statement and Appropriate Mining Framework 17
2.3.1 Co-Location Patterns 17
2.3.2 Related Work 19
2.3.3 Contributions and Novelties 21
2.4 The Novel Mining Solution 22
2.4.1 The Overall Mining Framework 22
2.4.2 Bit-String-Based Maximal Clique Enumeration 23
2.4.3 Constructing the Participating Instance Hash Table 28
2.4.4 Calculating Participation Indexes and Filtering MPCPs 30
2.4.5 The Analysis of Time and Space Complexities 32
2.5 Experiments 33
2.5.1 Data Sets 33
2.5.2 Experimental Objectives 34
2.5.3 Experimental Results and Analysis 34
2.6 Chapter Summary 47
3 Maximal Sub-prevalent Co-location Patterns 49
3.1 Introduction 49
3.2 Basic Concepts and Properties 51
3.3 A Prefix-Tree-Based Algorithm (PTBA) 54
3.3.1 Basic Idea 54
3.3.2 Algorithm 56
3.3.3 Analysis and Pruning 57
3.4 A Partition-Based Algorithm (PBA) 58
3.4.1 Basic Idea 58
3.4.2 Algorithm 62
3.4.3 Analysis of Computational Complexity 64
3.5 Comparison of PBA and PTBA 64
3.6 Experimental Evaluation 66
3.6.1 Synthetic Data Generation 67
3.6.2 Comparison of Computational Complexity Factors 67
3.6.3 Comparison of Expected Costs Involved in Identifying Candidates 69
3.6.4 Comparison of Candidate Pruning Ratio 69
3.6.5 Effects of the Parameter Clumpy 70
3.6.6 Scalability Tests 70
3.6.7 Evaluation with Real Data Sets 72
3.7 Related Work 75
3.8 Chapter Summary 77
4 SPI-Closed Prevalent Co-location Patterns 79
4.1 Introduction 79
4.2 Why SPI-Closed Prevalent Co-locations Improve Mining 81
4.3 The Concept of SPI-Closed and Its Properties 83
4.3.1 Classic Co-location Pattern Mining 83
4.3.2 The Concept of SPI-Closed 85
4.3.3 The Properties of SPI-Closed 86
4.4 SPI-Closed Miner 89
4.4.1 Preprocessing and Candidate Generation 89
4.4.2 Computing Co-location Instances and Their PI Values 93
4.4.3 The SPI-Closed Miner 93
4.5 Qualitative Analysis of the SPI-Closed Miner 95
4.5.1 Discovering the Correct SPI-Closed Co-location Set Ω 96
4.5.2 The Running Time of SPI-Closed Miner 96
4.6 Experimental Evaluation 96
4.6.1 Experiments on Real-life Data Sets 97
4.6.2 Experiments with Synthetic Data Sets 100
4.7 Related Work 104
4.8 Chapter Summary 105
5 Top-k Probabilistically Prevalent Co-location Patterns 107
5.1 Introduction 107
5.2 Why Mining Top-k Probabilistically Prevalent Co-location Patterns (Top-k PPCPs) 108
5.3 Definitions 110
5.3.1 Spatially Uncertain Data 110
5.3.2 Prevalent Co-locations 112
5.3.3 Prevalence Probability 113
5.3.4 Min_PI-Prevalence Probabilities 114
5.3.5 Top-k PPCPs 115
5.4 A Framework of Mining Top-k PPCPs 115
5.4.1 Basic Algorithm 115
5.4.2 Analysis and Pruning of Algorithm 5.1 116
5.5 Improved Computation of P(c， min_PI) 117
5.5.1 0-1-Optimization 117
5.5.2 The Matrix Method 118
5.5.3 Polynomial Matrices 122
5.6 Approximate Computation of P(c， min_PI) 125
5.7 Experimental Evaluations 128
5.7.1 Evaluation on Synthetic Data Sets 128
5.7.2 Evaluation on Real Data Sets 134
5.8 Chapter Summary 136
6 Non-redundant Prevalent Co-location Patterns 137
6.1 Introduction 137
6.2 Why We Need to Explore Non-redundant Prevalent Co-locations 139
6.3 Problem Definition 141
6.3.1 Semantic Distance 141
6.3.2 δ-Covered 143
6.3.3 The Problem Definition and Analysis 145
6.4 The RRclosed Method 148
6.5 The RRnull Method 150
6.5.1 The Method 150
6.5.2 The Algorithm 153
6.5.3 The Correctness Analysis 155
6.5.4 The Time Complexity Analysis 156
6.5.5 Comparative Analysis 157
6.6 Experimental Results 158
6.6.1 On the Three Real Data Sets 158
6.6.2 On the Synthetic Data Sets 161
6.7 Related Work 165
6.8 Chapter Summary 166
7 Dominant Spatial Co-location Patterns 167
7.1 Introduction 167
7.2 Why Dominant SCPs Are Useful to Mine 168
7.3 Related Work 171
7.4 Preliminaries and Problem Formulation 172
7.4.1 Preliminaries 173
7.4.2 Definitions 174
7.4.3 Formal Problem Formulation 179
7.4.4 Discussion of Progress 179
7.5 Proposed Algorithm for Mining Dominant SCPs 180
7.5.1 Basic Algorithm for Mining Dominant SCPs 180
7.5.2 Pruning Strategies 182
7.5.3 An Improved Algorithm 186
7.5.4 Comparison of Complexity 187
7.6 Experimental Study 188
7.6.1 Data Sets 188
7.6.2 Efficiency 189
7.6.3 Effectiveness 193
7.6.4 Real Applications 196
7.7 Chapter Summary 198
8 High Utility Co-location Patterns 201
8.1 Introduction 201
8.2 Why We Need High Utility Co-location Pattern Mining 202
8.3 Related W
1 Introduction 1
1.1 The Background and Applications 1
1.2 The Evolution and Development 5
1.3 The Challenges and Issues 7
1.4 Content and Organization of the Book 8
2 Maximal Prevalent Co-location Patterns 11
2.1 Introduction 11
2.2 Why the MCHT Method Is Proposed for Mining MPCPs 12
2.3 Formal Problem Statement and Appropriate Mining Framework 17
2.3.1 Co-Location Patterns 17
2.3.2 Related Work 19
2.3.3 Contributions and Novelties 21
2.4 The Novel Mining Solution 22
2.4.1 The Overall Mining Framework 22
2.4.2 Bit-String-Based Maximal Clique Enumeration 23
2.4.3 Constructing the Participating Instance Hash Table 28
2.4.4 Calculating Participation Indexes and Filtering MPCPs 30
2.4.5 The Analysis of Time and Space Complexities 32
2.5 Experiments 33
2.5.1 Data Sets 33
2.5.2 Experimental Objectives 34
2.5.3 Experimental Results and Analysis 34
2.6 Chapter Summary 47
3 Maximal Sub-prevalent Co-location Patterns 49
3.1 Introduction 49
3.2 Basic Concepts and Properties 51
3.3 A Prefix-Tree-Based Algorithm (PTBA) 54
3.3.1 Basic Idea 54
3.3.2 Algorithm 56
3.3.3 Analysis and Pruning 57
3.4 A Partition-Based Algorithm (PBA) 58
3.4.1 Basic Idea 58
3.4.2 Algorithm 62
3.4.3 Analysis of Computational Complexity 64
3.5 Comparison of PBA and PTBA 64
3.6 Experimental Evaluation 66
3.6.1 Synthetic Data Generation 67
3.6.2 Comparison of Computational Complexity Factors 67
3.6.3 Comparison of Expected Costs Involved in Identifying Candidates 69
3.6.4 Comparison of Candidate Pruning Ratio 69
3.6.5 Effects of the Parameter Clumpy 70
3.6.6 Scalability Tests 70
3.6.7 Evaluation with Real Data Sets 72
3.7 Related Work 75
3.8 Chapter Summary 77
4 SPI-Closed Prevalent Co-location Patterns 79
4.1 Introduction 79
4.2 Why SPI-Closed Prevalent Co-locations Improve Mining 81
4.3 The Concept of SPI-Closed and Its Properties 83
4.3.1 Classic Co-location Pattern Mining 83
4.3.2 The Concept of SPI-Closed 85
4.3.3 The Properties of SPI-Closed 86
4.4 SPI-Closed Miner 89
4.4.1 Preprocessing and Candidate Generation 89
4.4.2 Computing Co-location Instances and Their PI Values 93
4.4.3 The SPI-Closed Miner 93
4.5 Qualitative Analysis of the SPI-Closed Miner 95
4.5.1 Discovering the Correct SPI-Closed Co-location Set Ω 96
4.5.2 The Running Time of SPI-Closed Miner 96
4.6 Experimental Evaluation 96
4.6.1 Experiments on Real-life Data Sets 97
4.6.2 Experiments with Synthetic Data Sets 100
4.7 Related Work 104
4.8 Chapter Summary 105
5 Top-k Probabilistically Prevalent Co-location Patterns 107
5.1 Introduction 107
5.2 Why Mining Top-k Probabilistically Prevalent Co-location Patterns (Top-k PPCPs) 108
5.3 Definitions 110
5.3.1 Spatially Uncertain Data 110
5.3.2 Prevalent Co-locations 112
5.3.3 Prevalence Probability 113
5.3.4 Min_PI-Prevalence Probabilities 114
5.3.5 Top-k PPCPs 115
5.4 A Framework of Mining Top-k PPCPs 115
5.4.1 Basic Algorithm 115
5.4.2 Analysis and Pruning of Algorithm 5.1 116
5.5 Improved Computation of P(c， min_PI) 117
5.5.1 0-1-Optimization 117
5.5.2 The Matrix Method 118
5.5.3 Polynomial Matrices 122
5.6 Approximate Computation of P(c， min_PI) 125
5.7 Experimental Evaluations 128
5.7.1 Evaluation on Synthetic Data Sets 128
5.7.2 Evaluation on Real Data Sets 134
5.8 Chapter Summary 136
6 Non-redundant Prevalent Co-location Patterns 137
6.1 Introduction 137
6.2 Why We Need to Explore Non-redundant Prevalent Co-locations 139
6.3 Problem Definition 141
6.3.1 Semantic Distance 141
6.3.2 δ-Covered 143
6.3.3 The Problem Definition and Analysis 145
6.4 The RRclosed Method 148
6.5 The RRnull Method 150
6.5.1 The Method 150
6.5.2 The Algorithm 153
6.5.3 The Correctness Analysis 155
6.5.4 The Time Complexity Analysis 156
6.5.5 Comparative Analysis 157
6.6 Experimental Results 158
6.6.1 On the Three Real Data Sets 158
6.6.2 On the Synthetic Data Sets 161
6.7 Related Work 165
6.8 Chapter Summary 166
7 Dominant Spatial Co-location Patterns 167
7.1 Introduction 167
7.2 Why Dominant SCPs Are Useful to Mine 168
7.3 Related Work 171
7.4 Preliminaries and Problem Formulation 172
7.4.1 Preliminaries 173
7.4.2 Definitions 174
7.4.3 Formal Problem Formulation 179
7.4.4 Discussion of Progress 179
7.5 Proposed Algorithm for Mining Dominant SCPs 180
7.5.1 Basic Algorithm for Mining Dominant SCPs 180
7.5.2 Pruning Strategies 182
7.5.3 An Improved Algorithm 186
7.5.4 Comparison of Complexity 187
7.6 Experimental Study 188
7.6.1 Data Sets 188
7.6.2 Efficiency 189
7.6.3 Effectiveness 193
7.6.4 Real Applications 196
7.7 Chapter Summary 198
8 High Utility Co-location Patterns 201
8.1 Introduction 201
8.2 Why We Need High Utility Co-location Pattern Mining 202
8.3 Related W
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