swMATH ID: 29979
Software Authors: Rizoiu, Marian-Andrei; Velcin, Julien; Bonnevay, Stéphane; Lallich, Stéphane
Description: ClusPath: a temporal-driven clustering to infer typical evolution paths. 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.
Homepage: https://link.springer.com/article/10.1007%2Fs10618-015-0445-7
Keywords: detection of long-term trends; evolutionary clustering; temporal clustering; temporal cluster graph; semi-supervised clustering; Pareto front estimation
Related Software: SPEA2
Cited in: 1 Publication

Citations by Year