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Article Dans Une Revue Data Mining and Knowledge Discovery Année : 2016

ClusPath: a temporal-driven clustering to infer typical evolution paths

Résumé

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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Dates et versions

hal-01665546 , version 1 (12-12-2018)

Identifiants

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Marian-Andrei Rizoiu, Julien Velcin, Stéphane Bonnevay, Stéphane Lallich. ClusPath: a temporal-driven clustering to infer typical evolution paths. Data Mining and Knowledge Discovery, 2016, 30 (5), pp.1324 - 1349. ⟨10.1007/s10618-015-0445-7⟩. ⟨hal-01665546⟩
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