MONIC: modeling and monitoring cluster transitions

By Myra Spiliopoulou, Eirini Ntoutsi, Yannis Theodoridis, R. Schult. Knowledge Discovery and Data Mining, 2006

314 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/1141b3de7d6394d423ee25e035aba2eb61b63d9c

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Citations in the corpus

Detecting communities and their evolutions in dynamic social networks—a Bayesian approach (2011, methodology): “Spiliopoulou et al. (2006) proposed a framework, MONIC, to model and monitor cluster transitions over time.”
Analyzing communities and their evolutions in dynamic social networks (2009, background): “…there has been a growing body of work on the analysis of communities and their temporal evolution in dynamic networks [Asur et al. 2007; Kumar et al. 2003; Kumar et al. 2006; Leskovec et al. 2005; Lin et al. 2007; Palla et al. 2007; Spiliopoulou et al. 2006; Toyoda and Kitsuregawa 2003].”
Facetnet: a framework for analyzing communities and their evolutions in dynamic networks (2008, methodology): “More recently, there has been a growing body of work on the analysis of communities and their temporal evolution in dynamic networks [1, 8, 9, 10, 11, 16, 19, 21].”
A survey on data stream clustering and classification (2015, methodology): “proposed the MONIC algorithm to detect changes in clusters by checking their overlaps [89].”
The ClusTree: indexing micro-clusters for anytime stream mining (2011, methodology): “We show that our algorithm can be combined with existing techniques for aging objects in the stream using decay functions, reporting cluster snapshots at different points in time, and comparing views at different points in time [2,3,25].”
Discovery of convoys in trajectory databases (2008, background): “, and c3 become a moving cluster. However, this is not a convoy. t 1 2 3 (a) ( b) o 1 o 2 o 3 o 4 x y o 1 oo 3 o x c 1 c 2 c 3 c 3 c 2 c 1 Figure 2: Convoys Versus Moving Clusters Spiliopoulou et al. [24] study transitions in moving clusters (e.g., disappearance and splitting) between consecutive ti”
Social Network Data Analytics (2011, methodology): “The study of such complex relationship networks, recently referred to as network science, can provide insight into their structures, properties and emergent behaviors [11, 12, 41, 77].”
Data stream clustering: A survey (2013, methodology): “MONIC: Modeling and monitoring cluster transitions.”