Trajectory pattern mining

By Fosca Giannotti, Mirco Nanni, Fabio Pinelli, Dino Pedreschi. Knowledge Discovery and Data Mining, 2007

1,171 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/3b3ed6535263aa4c7a1381ff146c2e16b74f54b2

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

In Information Systems

In Signal Processing

Citations in the corpus

Trajectory Data Mining (2015, methodology): “It was used to evaluate trajectory pattern mining task in [34].”
Mining Individual Life Pattern Based on Location History (2009, methodology): “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].”
Mining user similarity based on location history (2008, background): “[6] developed an extension of the sequential pattern mining paradigm that analyzes the trajectories of moving objects.”
A Survey on Trajectory Data Management, Analytics, and Learning (2020, background): “of recommendations for restaurants, attractions, or public event to tourists. The three most common categories of interests discovery using trajectory data for tourism are: 1) point of interest (POI) [71]; 2) region of interest (ROI) [90, 158]; and 3) interactive discovery [25, 171]. Palma et al. [1”
Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detection (2017, background): “Trajectory Clustering When considering approaches for learning motion patterns through clustering, Giannotti et al. (2007) have proposed the concept of “trajectory patterns”, which represents the descriptions of frequent behaviours in terms of space and time.”
A Hybrid Prediction Model for Moving Objects (2008, background): “[9], [17] define and mine sequential patterns of an object's trajectories.”
DeepMove: Predicting Human Mobility with Attentional Recurrent Networks (2018, methodology): “Early methods for mobility prediction are mostly pattern-based [4, 18, 25, 26, 42].”
A clustering-based approach for discovering interesting places in trajectories (2008, background): “Several works for trajectory clustering have been developed to find similar subtrajectories or dense regions as [9], [11], [13].”
Smart cities of the future (2012, neutral): “Much research has begun to show that the semantic deﬁciency of big mobility data can be bridged by their size and precision [23].”
Unveiling the complexity of human mobility by querying and mining massive trajectory data (2011, background): “Originally introduced in [17], a T-Pattern (Fig.”
Discovering popular routes from trajectories (2011, background): “The search of popular routes in light of past movements is highly relevant to trajectory processing/querying issues, including pattern mining [7], [2], [1], [8], trajectory clustering [9], [10], hot route discovery [11], [12], trajectory prediction [3], [4], [13], etc.”
Sequential pattern mining -- approaches and algorithms (2013, methodology): “Guralnik and Karypis [2004] developed two static distribution algorithms, based on the tree projection algorithm for frequent itemset discovery [Agrawal et al.”
Travel route recommendation using geotags in photo sharing sites (2010, methodology): “Spatio-temporal sequential pattern mining from GPS trajectories was also performed by Giannotti et al. [24].”
WhereNext: a location predictor on trajectory pattern mining (2009, methodology): “T-pattern was originally introduced in [3], where the authors developed an extension of the sequential pattern mining paradigm, introduced in [2], which analyzes the trajectories of moving objects.”
Urban Computing: Concepts, Methodologies, and Applications (2014, background): “Giannotti et al. [2007] is the first research targeting at this problem.”