Entrepôts, Représentation et Ingénierie des Connaissances
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- ClusPath: a temporal-driven clustering to infer typical evolution paths

Author(s): Rizoiu M., Velcin J.(Corresp.), Bonnevay S.(Corresp.), Lallich S.(Corresp.)

(Article) Published: Data Mining And Knowledge Discovery, vol. 30 p.1324-1349 (2016)


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.