Abstract
We propose ClusPath, a novel algorithm for detecting general evolution tendencies in a population of entities. We show how abstract notions, such as the Swedish socio-economical model (in a political dataset) or the companies fiscal optimization (in an economical dataset) can be inferred from low-level descriptive features. Such high-level regularities in the evolution of entities are detected by combining spatial and temporal features into a spatio-temporal dissimilarity measure and using semi-supervised clustering techniques. The relations between the evolution phases are modeled using a graph structure, inferred simultaneously with the partition, by using a “slow changing world” assumption. The idea is to ensure a smooth passage for entities along their evolution paths, which catches the long-term trends in the dataset. Additionally, we also provide a method, based on an evolutionary algorithm, to tune the parameters of ClusPath to new, unseen datasets. This method assesses the fitness of a solution using four opposed quality measures and proposes a balanced compromise.
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Notes
Download pretreated version of the CPDS1 dataset here: http://goo.gl/17ihsf.
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NICTA is funded by the Australian Government through the Department of Communications and the Australian Research Council through the ICT Centre of Excellence Program.
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Responsible editors: Joao Gama, Indre Zliobaite , Alipio Jorge, Concha Bielza.
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Rizoiu, MA., Velcin, J., Bonnevay, S. et al. ClusPath: a temporal-driven clustering to infer typical evolution paths. Data Min Knowl Disc 30, 1324–1349 (2016). https://doi.org/10.1007/s10618-015-0445-7
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DOI: https://doi.org/10.1007/s10618-015-0445-7