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

TL;DR

ClusPath is a new clustering algorithm that detects typical evolution paths of entities over time by combining spatial and temporal features and modeling phase relations with a graph structure.

Contribution

The paper introduces ClusPath, a novel semi-supervised clustering method that infers high-level evolution regularities from low-level features using a spatio-temporal dissimilarity measure and a graph-based phase relation model.

Findings

Successfully infers socio-economic and corporate evolution patterns.

Ensures smooth entity transitions along evolution paths.

Provides an evolutionary algorithm for parameter tuning.

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.