ClusPath: A Temporal-driven Clustering to Infer Typical Evolution Paths

Marian-Andrei Rizoiu, Julien Velcin, Stéphane Bonnevay, Stéphane Lallich

We propose ClusPath, a novel algorithm for detecting general evolution tendencies in a population of individuals. 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 ClusPath’s parameters to new, unseen datasets. This method assesses the fitness of a solution using four opposed quality measures and proposes a balanced compromise.

Keywords: detection of long-term trends, evolutionary clustering, temporal clustering, temporal cluster graph, semi-supervised clustering, Pareto front estimation


Status: published
Type: Journal Publication
Conference/location: Data Mining and Knowledge Discovery
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