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- Title
ClusPath: a temporal-driven clustering to infer typical evolution paths.
- Authors
Rizoiu, Marian-Andrei; Velcin, Julien; Bonnevay, Stéphane; Lallich, Stéphane
- 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.
- Subjects
EVOLUTIONARY algorithms; SUPERVISED learning; CLUSTER analysis (Statistics); DATA mining; ECONOMIC models
- Publication
Data Mining & Knowledge Discovery, 2016, Vol 30, Issue 5, p1324
- ISSN
1384-5810
- Publication type
Article
- DOI
10.1007/s10618-015-0445-7