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

• Autores: Marian-Andrei Rizoiu, Julien Velcin, Stéphane Bonnevay, Stéphane Lallich
• Localización: Data mining and knowledge discovery, ISSN 1384-5810, Vol. 30, Nº 5, 2016, págs. 1324-1349
• Idioma: inglés
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• Resumen

  • 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.