1 Introduction
1.1 The Background and Applications
1.2 The Evolution and Development
1.3 The Challenges and Issues
1.4 Content and Organization of the Book
2 Maximal Prevalent Co-location Patterns
2.1 Introduction
2.2 Why the MCHT Method Is Proposed for Mining MPCPs
2.3 Formal Problem Statement and Appropriate Mining Framework
2.3.1 Co-Location Patterns
2.3.2 Related Work
2.3.3 Contributions and Novelties
2.4 The Novel Mining Solution
2.4.1 The Overall Mining Framework
2.4.2 Bit-String-Based Maximal Clique Enumeration
2.4.3 Constructing the Participating Instance Hash Table
2.4.4 Calculating Participation Indexes and Filtering MPCPs
2.4.5 The Analysis of Time and Space Complexities
2.5 Experiments
2.5.1 Data Sets
2.5.2 Experimental Objectives
2.5.3 Experimental Results and Analysis
2.6 Chapter Summary
3 Maximal Sub-prevalent Co-location Patterns
3.1 Introduction
3.2 Basic Concepts and Properties
3.3 A Prefix-Tree-Based Algorithm (PTBA)
3.3.1 Basic Idea
3.3.2 Algorithm
3.3.3 Analysis and Pruning
3.4 A Partition-Based Algorithm (PBA)
3.4.1 Basic Idea
3.4.2 Algorithm
3.4.3 Analysis of Computational Complexity
3.5 Comparison of PBA and PTBA
3.6 Experimental Evaluation
3.6.1 Synthetic Data Generation
3.6.2 Comparison of Computational Complexity Factors
3.6.3 Comparison of Expected Costs Involved in Identifying Candidates
3.6.4 Comparison of Candidate Pruning Ratio
3.6.5 Effects of the Parameter Clumpy
3.6.6 Scalability Tests
3.6.7 Evaluation with Real Data Sets
3.7 Related Work
3.8 Chapter Summary
4 SPI-Closed Prevalent Co-location Patterns
4.1 Introduction
4.2 Why SPI-Closed Prevalent Co-locations Improve Mining
4.3 The Concept of SPI-Closed and Its Properties
4.3.1 Classic Co-location Pattern Mining
4.3.2 The Concept of SPI-Closed
4.3.3 The Properties of SPI-Closed
4.4 SPI-Closed Miner
4.4.1 Preprocessing and Candidate Generation
4.4.2 Computing Co-location Instances and Their PI Values
4.4.3 The SPI-Closed Miner
4.5 Qualitative Analysis of the SPI-Closed Miner
4.5.1 Discovering the Correct SPI-Closed Co-location Set Ω
4.5.2 The Running Time of SPI-Closed Miner
4.6 Experimental Evaluation
4.6.1 Experiments on Real-life Data Sets
4.6.2 Experiments with Synthetic Data Sets
4.7 Related Work
4.8 Chapter Summary
5 Top-k Probabilistically Prevalent Co-location Patterns
5.1 Introduction
5.2 Why Mining Top-k Probabilistically Prevalent Co-location Patterns (Top-k PPCPs)
5.3 Definitions
5.3.1 Spatially Uncertain Data
5.3.2 Prevalent Co-locations
5.3.3 Prevalence Probability
5.3.4 Min_PI-Prevalence Probabilities
5.3.5 Top-k PPCPs
5.4 A Framework of Mining Top-k PPCPs
5.4.1 Basic Algorithm
5.4.2 Analysis and Pruning of Algorithm 5.
5.5 Improved Computation of P(c, min_PI)
5.5.1 0-1-Optimization
5.5.2 The Matrix Method
5.5.3 Polynomial Matrices
5.6 Approximate Computation of P(c, min_PI)
5.7 Experimental Evaluations
5.7.1 Evaluation on Synthetic Data Sets
5.7.2 Evaluation on Real Data Sets
5.8 Chapter Summary
6 Non-redundant Prevalent Co-location Patterns
6.1 Introduction
6.2 Why We Need to Explore Non-redundant Prevalent Co-locations
6.3 Problem Definition
6.3.1 Semantic Distance
6.3.2 δ-Covered
6.3.3 The Problem Definition and Analysis
6.4 The RRclosed Method
6.5 The RRnull Method
6.5.1 The Method
6.5.2 The Algorithm
6.5.3 The Correctness Analysis
6.5.4 The Time Complexity Analysis
6.5.5 Comparative Analysis
6.6 Experimental Results
6.6.1 On the Three Real Data Sets
6.6.2 On the Synthetic Data Sets
6.7 Related Work
6.8 Chapter Summary
7 Dominant Spatial Co-location Patterns
7.1 Introduction
7.2 Why Dominant SCPs Are Useful to Mine
7.3 Related Work
7.4 Preliminaries and Problem Formulation
7.4.1 Preliminaries
7.4.2 Definitions
7.4.3 Formal Problem Formulation
7.4.4 Discussion of Progress
7.5 Proposed Algorithm for Mining Dominant SCPs
7.5.1 Basic Algorithm for Mining Dominant SCPs
7.5.2 Pruning Strategies
7.5.3 An Improved Algorithm
7.5.4 Comparison of Complexity
7.6 Experimental Study
7.6.1 Data Sets
7.6.2 Efficiency
7.6.3 Effectiveness
7.6.4 Real Applications
7.7 Chapter Summary
8 High Utility Co-location Patterns
8.1 Introduction
8.2 Why We Need High Utility Co-location Pattern Mining
8.3 Related Work
8.3.1 Spatial Co-location Pattern Mining
8.3.2 Utility Itemset Mining
8.4 Problem Definition
8.5 A Basic Mining Approach
8.6 Extended Pruning Approach
8.6.1 Related Definitions
8.6.2 Extended Pruning Algorithm (EPA)
8.7 Partial Pruning Approach
8.7.1 Related Definitions
8.7.2 Partial Pruning Algorithm (PPA)
8.8 Experiments
8.8.1 Differences Between Mining Prevalent SCPs and High Utility SCPs
8.8.2 Effect of the Number of Total Instances n
8.8.3 Effect of the Distance Threshold d
8.8.4 Effect of the Pattern Utility Ratio Threshold ξ
8.8.5 Effect of s in vss
8.8.6 Comparing PPA and EPA with a Different Utility Ratio Threshold ξ
8.9 Chapter Summary
9 High Utility Co-location Patterns with Instance Utility
9.1 Introduction
9.2 Why We Need Instance Utility with Spatial Data
9.3 Related Work
9.4 Related Concepts
9.5 A Basic Algorithm
9.6 Pruning Strategies
9.7 Experimental Analysis
9.7.1 Data Sets
9.7.2 The Quality of Mining Results
9.7.3 Evaluation of Pruning Strategies
9.8 Chapter Summary
10 Interactively Post-mining User-Preferred Co-location Patterns with a Probabilistic Model
10.1 Introduction
10.2 Why We Need Interactive Probabilistic Post-mining
10.3 Related Work
10.4 Problem Statement
10.4.1 Basic Concept
10.4.2 Subjective Preference Measure
10.4.3 Formal Problem Statement
10.5 Probabilistic Model
10.5.1 Basic Assumptions
10.5.2 Probabilistic Model
10.5.3 Discussion
10.6 The Complete Algorithm
10.6.1 The Algorithm
10.6.2 Two Optimization Strategies
10.6.3 The Time Complexity Analysis
10.7 Experimental Results
10.7.1 Experimental Setting
10.7.2 The Simulator
10.7.3 Accuracy Evaluation on Real Data Sets
10.7.4 Accuracy Evaluation on Synthetic Data Sets
10.7.5 Sample Co-location Selection
10.8 Chapter Summary
11 Vector-Degree: A General Similarity Measure for Co-location Patterns
11.1 Introduction
11.2 Why We Measure the Similarity Between SCPs
11.3 Preliminaries
11.3.1 Spatial Co-location Pattern (SCP)
11.3.2 A Toy Example
11.3.3 Problem Statement
11.4 The Method
11.4.1 Maximal Cliques Enumeration Algorithm
11.4.2 A Representation Model of SCPs
11.4.3 Vector-Degree: the Similarity Measure of SCPs
11.4.4 Grouping SCPs Based on Vector-Degree
11.5 Experimental Evaluations
11.5.1 Data Sets
11.5.2 Results
11.6 Chapter Summary
References
本书以应用需求（领域驱动）为导向，系统介绍了本书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。
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