Trajectory clustering: a partition-and-group framework

By Jae-Gil Lee, Jiawei Han, Kyu-Young Whang. ACM SIGMOD Conference, 2007

1,603 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/287b6e2ad1225b5a8291d8ccf150ab1ecdf2af7f

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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].”
Discovery of convoys in trajectory databases (2008, methodology): “[20] point out that the above distance measures captur e the global similarity between two trajectories, but not their l ocal similarity during a short time interval.”
Spatio-Temporal Data Mining (2017, methodology): “For example, clustering of trajectories has been used to find groups of hurricanes with similar trajectories [Lee et al. 2007], groups of taxi traces that follow a similar route [Liu et al. 2010], or groups of moving objects that follow the same motion in video streams [Gaffney and Smyth 1999].”
A review of moving object trajectory clustering algorithms (2016, background): “Lee et al. (2007) provide a partition-and-group framework for trajectory clustering.”
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].”
Trajectory Data Mining (2015, methodology): “Similarly, Lee et al. [2007] proposed to partition a trajectory by using the concept of Minimal Description Language (MDL), which is comprised of two components: L ( H ) and L ( D H ).”
Discovering popular routes from trajectories (2011, methodology): “It is essentially a trajectory clustering algorithm based on traffic density, which shares the same idea with [ 9 ] and [10] that cluster trajectories by line segment density.”
Efficient agglomerative hierarchical clustering (2015, background): “In Lee et al. (2007), a partition-and-group framework was proposed to discover common sub-trajectories from a trajectory database using trajectory-based clustering which is combined with a proposed region-based clustering in Lee et al. (2008).”
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?”.”
TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering (2008, methodology): “The procedure of trajectory-based clustering is based on the partition-and-group framework proposed by Lee et al. [12].”
Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detection (2017, background): “De-viating from discovering common trajectories, Lee et al. (2007) proposed to discover common sub-trajectories using a partition-and-group framework.”
Trajectory Clustering and an Application to Airspace Monitoring (2010, background): “[6] presented a partition-and-group framework for trajectory clustering.”
Swarm (2010, background): “[17], distance measure established on whole trajectories may miss interesting common paths in sub-trajectories.”
Unveiling the complexity of human mobility by querying and mining massive trajectory data (2011, neutral): “It is no surprise, then, that finding answers to these questions is beyond the limits of the current generation of commercial systems, and cannot even be accomplished by simply applying known research prototypes, such as the mobility data mining methods developed within GeoPKDD by the authors of thi”