K-Means Algorithm, Influenza Transmission, Cluster Analysis, Urban Characteristics Share and Cite: Ye, S. (2025) Application ...

ml-knn-kmeans/ ├── knn_model.py # KNN classification ├── kmeans_clustering.py # K-Means clustering ├── dataset.csv # Dataset used ├── README.md # Project documentation --- ## 🛠️ Technologies Used - ...

This study investigates the application of advanced clustering methods to geological fracture analysis in the Baba Kohi anticline, located in the folded Zagros region of southwest Iran. The primary ...

Clustering is an unsupervised machine learning technique used to organize unlabeled data into groups based on similarity. This paper applies the K-means and Fuzzy C-means clustering algorithms to a ...

In elbow method, you start with some k,say k=2,and we try to compute sum of square error.Sum of square error means you try to compute the distance of individual data ...

Abstract: The traditional K-means algorithm often leads to unstable clustering quality due to the randomness of the initial clustering center selection and tends to fall into suboptimal solutions when ...

1 Northwest Branch of Research Institute of Petroleum Exploration and Development, PetroChina, Lanzhou, China 2 Xinjiang Oilfield Company, PetroChina, Karamay, China Automatic picking of seismic ...

In cognitive diagnostic assessment (CDA), clustering analysis is an efficient approach to classify examinees into attribute-homogeneous groups. Many researchers have proposed different methods, such ...