Clustering

k-Means Clustering

k-Means Clustering is a clustering algorithm that divides a training set into $k$ different clusters of examples that are near each other. It works by initializing $k$ different centroids {$\mu\left(1\right),\ldots,\mu\left(k\right)$} to different values, then alternating between two steps until convergence:

(i) each training example is assigned to cluster $i$ where $i$ is the index of the nearest centroid $\mu^{(i)}$

(ii) each centroid $\mu^{(i)}$ is updated to the mean of all training examples $x^{(j)}$ assigned to cluster $i$.

Text Source: Deep Learning, Goodfellow et al

Image Source: scikit-learn

Papers


Paper Code Results Date Stars

Tasks


Task Papers Share
Clustering 167 16.04%
Object Detection 109 10.47%
Object 50 4.80%
Autonomous Driving 23 2.21%
Semantic Segmentation 21 2.02%
Deep Learning 16 1.54%
Self-Supervised Learning 15 1.44%
Dimensionality Reduction 14 1.34%
Quantization 13 1.25%

Components


Component Type
🤖 No Components Found You can add them if they exist; e.g. Mask R-CNN uses RoIAlign

Categories