The basic K-means is sensitive to the initial centre and easy to get stuck at local optimal value. To solve such problems, a new clustering algorithm is proposed based on simulated annealing. The ...

It's a Python application that sorts images by their similarity based on ConvNext deep learning features and HSV histogram comparison. Embeddings are pre-clustered with K-Means. FAISS index assigned ...

K-means is one of the most simple and popular clustering algorithms, which implemented as a standard clustering method in most of machine learning researches. The goal of K-means clustering is finding ...

Implement the K-Means Clustering algorithm from scratch using NumPy and visualize the results with Matplotlib. Why it's a good addition: It's a foundational unsupervised learning algorithm that fits ...