On Discovering Moving Clusters in Spatio-temporal Data

By Panos Kalnis, Nikos Mamoulis, Spiridon Bakiras. International Symposium on Spatial and Temporal Databases, 2005

490 citations recorded by the source corpus.

Subfields: Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/02947381dc4addc11b17cb5ebfb9f409e55c707d

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Citations in the corpus

Trajectory pattern mining (2007, background): “of trajectory segments and searches approximate instances in the data; on the opposite, the work in [7] provides a clustering-based perspective, and considers patterns in the form of moving regions within time intervals, such as spatiotemporal cylinders or tubes and counts as occurrences all traject”
Swarm (2010, background): “Another similar definition, moving cluster [14], tries to find a group of moving objects which have considerably portion of overlap at any two consecutive timestamps.”
Visual Traffic Jam Analysis Based on Trajectory Data (2013, background): “They have studied different kinds of patterns [23] [26].”
Discovery of convoys in trajectory databases (2008, methodology): “Other relevant work concerns the clustering of moving objects [17, 19, 21].”
Towards a taxonomy of movement patterns (2008, background): “A flock of migrating geese, a convoy of cars following the same route, and troops that move parallel on a military battlefield are different examples of moving clusters [14, 10, 7].”
Unveiling the complexity of human mobility by querying and mining massive trajectory data (2011, unknown): “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”
Spatio-Temporal Data Mining (2017, methodology): “Algorithms for detecting moving clusters of trajectories have been developed in [Kalnis et al. 2005] and in subsequent studies [Dodge et al. 2008; Jeung et al. 2008; Li et al. 2010].”
MONIC: modeling and monitoring cluster transitions (2006, background): “Kalnis et al propose a special type of cluster change, the “moving cluster”, whose contents may change while its density function remains the same during its lifetime [6].”
Visual Analytics of Movement (2013, background): “be found in the paper by Kwan (2000). In analysing movements of people, she transforms their spatial positions to distances from their homes in order to compare how far different people travel and on what distances from the homes they work, shop, and perform other activities.”
Social media and political communication: a social media analytics framework (2012, background): “Researchers have also shown interest in discovering changing clusters in dynamic data (Kalnis et al. 2005) and clustering the evolving data streams (Aggarwal et al.”
A model for enriching trajectories with semantic geographical information (2007, background): “2 The Problem of Trajectory Data Analysis The a priori integration of trajectory data with the background geographic information may lead to the discovery of semantic trajectory patterns that many data mining techniques [4, 9, 11, 14, 16] that consider trajectories as sample points (tid, x, y, t) ma”