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Table of contents
1 Introduction
1.1 Illustrative Example and Motivations
1.2 Contributions
2 Related Work
2.1 Preliminary Definitions
2.2 Object Movement Pattern Mining
3 All in One: Mining Multiple Movement Patterns
3.1 Object Movement Patterns in Itemset Context
3.2 Frequent Closed Itemset-based ObjectMovement PatternMining Algorithm
3.2.1 GeT_Move
3.2.2 Incremental GeT_Move
3.3 Preliminarily Experimental Results
3.3.1 Effectiveness
3.3.2 Efficiency
3.3.3 Toward A Parameter Free Incremental GeT_Move Algorithm
3.3.4 Object Movement Pattern Mining Algorithm Based on Explicit Combination of FCI Pairs
3.4 Experimental Results
3.4.1 Parameter Free Incremental GeT_Move Efficiency
3.4.2 Movement Pattern Mining Algorithm Based on Explicit Combination of FCI Pairs
3.5 Discussion
4 Mining Fuzzy Moving Object Clusters
4.1 Introduction
4.2 Fuzzy Closed Swarms
4.3 Discovering of Fuzzy Closed Swarms
4.4 Experimental Results
4.4.1 Effectiveness
4.4.2 Parameter Sensitiveness
4.5 Discussion
5 Mining Time Relaxed Gradual Moving Object Clusters
5.1 Introduction
5.2 Problem Statement
5.3 Discovering Maximal Time Relaxed Gradual Trajectory Patterns
5.3.1 ClusterGrowth Approach
5.3.2 The ClusterGrowth Implementation
5.4 Preliminarily Experimental Results
5.4.1 Effectiveness and PatternMeaning
5.4.2 Parameter Sensitiveness
5.5 Mining Representative Gradual Trajectory Patterns
5.5.1 Problem Statement
5.5.2 Encoding Scheme
5.5.3 Complexity Analysis
5.5.4 Mining top-K Representative rGpatterns
5.6 Experimental Results onMining Representative rGpatterns
5.7 Discussion
6 Mining Representative Movement Patterns through Compression
6.1 Introduction
6.2 Problem Statement
6.3 Encoding Scheme
6.3.1 Movement Pattern Dictionary-based Encoding
6.3.2 OverlappingMovement Pattern Encoding
6.4 Mining Compression ObjectMovement Patterns
6.4.1 Naive Greedy Approach
6.4.2 Smart Greedy Approach
6.5 Experimental Results
6.6 Discussion
7 MiningMulti-Relational Gradual Patterns
7.1 Introduction
7.2 Preliminarily Definitions
7.2.1 Multi-Relational Data
7.2.2 Gradual Pattern: Single Relation vsMulti-Relations
7.2.3 Multi-Relational Gradual Pattern
7.3 Pattern Occurrences
7.4 Pattern Support
7.4.1 Kendall’s ⌧-basedMulti-Relational Gradual Pattern Support
7.4.2 Gradual Support
7.5 Multi-Relational Gradual PatternMining Algorithms
7.5.1 MiningMono-Relational Gradual Patterns
7.5.2 DiscoveringMulti-Relational Gradual Patterns
7.6 Experimental Results
7.6.1 Multi-Relational Gradual Patterns
7.6.2 Efficiency and Pattern Distribution
7.7 RelatedWork
7.8 Discussion
8 Applications
8.1 Introduction
8.2 The MULTI_MOVE System Architecture
8.3 Other Applications
8.3.1 Mining Trajectories on Genes
8.3.2 Mining Trajectories on Tweets
8.4 Discussion
9 Conclusion & Perspectives
9.1 Conclusion
9.2 Streaming GeT_Move: Mining Representative Movement Patterns from Streaming Trajectory Data
9.3 CorGpattern: Combined Time Relaxed Gpattern
9.3.1 CorGpattern Definition
9.3.2 CoClusterGrowth: DiscoveringMaximal CorGpatterns
9.4 DirectlyMining RepresentativeMovement Patterns through Compression
9.5 CompletedMiningMulti-Relational Gradual Patterns
9.6 TrajectoryMining on Diverse Applications
9.6.1 Social Networks and SocialMedia
9.6.2 Remote Sensing, Spatial Information on Satellite Image Processing
10 Publications
10.1 International Conferences and Journals
Bibliography
