Identifying the Behaviors of an Industrial Plant: Application to Industry 4.0. - Dylan Molinié; Kurosh Madani; Corentin Amarger (IDAACS, 2021)

Characterizing N-Dimension Data Clusters: A Density-based Metric for Compactness and Homogeneity Evaluation. - Dylan Molinié; Kurosh Madani (IN4PL, 2021)

HyDensity: A Hyper-Volume-Based Density Metric for Automatic Cluster Evaluation. - Dylan Molinié; Kurosh Madani; Abdennasser Chebira (IN4PL (Revised Selected Papers), 2021)

BSOM: A Two-Level Clustering Method Based on the Efficient Self-Organizing Maps. - Dylan Molinié; Kurosh Madani (ICCAD, 2022)

ECD Test: An Empirical Way based on the Cumulative Distributions to Evaluate the Number of Clusters for Unsupervised Clustering. - Dylan Molinié; Kurosh Madani (IN4PL, 2022)

Clustering at the Disposal of Industry 4.0: Automatic Extraction of Plant Behaviors. - Dylan Molinié; Kurosh Madani; Véronique Amarger (Sensors, 2022)

Behavioral Modeling of Real Dynamic Processes in an Industry 4.0-Oriented Context. - Dylan Molinié; Kurosh Madani; Véronique Amarger (DATA, 2023)

Unsupervised Clustering at the Service of Automatic Anomaly Detection in Industry 4.0. - Dylan Molinié; Kurosh Madani; Véronique Amarger (IWANN (2), 2023)

Identifying the Regions of a Space with the Self-Parameterized Recursively Assessed Decomposition Algorithm (SPRADA). - Dylan Molinié; Kurosh Madani; Véronique Amarger; Abdennasser Chebira (Mach. Learn. Knowl. Extr., 2023)