Mining frequent spatio-temporal sequential patterns

By H. Cao, Nikos Mamoulis, David Wai-Lok Cheung. Industrial Conference on Data Mining, 2005

331 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/055c2d9bdb09f7d728fa875eedd30e465ae2e6ac

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Yu Zheng
2. Philip S. Yu
3. Hong Cheng
4. Jiawei Han
5. Xifeng Yan
6. Xing Xie
7. Dong Xin
8. Manolis Terrovitis

In Information Systems

1. Yu Zheng
2. Philip S. Yu
3. Hong Cheng
4. Jiawei Han
5. Xifeng Yan
6. Dong Xin
7. Xing Xie
8. Dino Pedreschi

In Signal Processing

1. Yu Zheng
2. Xing Xie
3. Philip S. Yu
4. Jiawei Han
5. Vipin Kumar
6. Anuj Karpatne
7. Jing Yuan
8. Hong Cheng

Citations in the corpus

• Mining Individual Life Pattern Based on Location History (2009, background): “On this topic, some literatures aim at performing traditional data mining tasks on spatial-temporal data, like classification [1], clustering [2], pattern mining [3], [4] and outlier detection [5].”
• Privacy Preservation in the Publication of Trajectories (2008, methodology): “For example he could apply some data mining operation (like clustering [10] or sequence pattern mining [5]) to the resulting trajectories, or perform aggregate queries of the form “How many people traveled from region X to region Y and then to region Z?”.”
• Towards a taxonomy of movement patterns (2008, background): “6 , 22 , 23 Another example is the sequential order of fixations for several runs of an eye movement experiment.”
• Discovering spatio-temporal causal interactions in traffic data streams (2011, background): “However to the best of our knowl­edge, the discovery of relationships, especially causal inter­actions, among detected tra.c outliers has not been inves­tigated before.”
• Citywide traffic congestion estimation with social media (2015, background): “Mining frequent spatio-temporal sequential patterns has been a focused theme in data mining research for decades [2, 21].”
• Trajectory pattern mining (2007, background): “The work in [3] considers patterns that are in the form”
• Spatio-Temporal Data Mining (2017, methodology): “A similar approach for sequential pattern mining was developed in [Cao et al. 2005] where pattern elements were defined as spatial regions around frequent line segments of trajectories.”
• Trajectory Data Mining (2015, methodology): “1 Sequential Pattern Mining in a Free Space Line-simplification-based methods: An early solution aiming to deal with the aforementioned issues was proposed in 2005 [11].”
• Frequent pattern mining: current status and future directions (2007, background): “Such optimization ideas can be extended to mining spatiotemporal sequential patterns as well, as shown in Cao et al. (2005).”