I developed a Machine Learning algorithm called KOAC or Kernel-induced Online Agglomerative Clustering
- The general idea is to increase the computational power of traditional linear Machine Learning algos by mapping the data into a high-dimensional feature space.
- This technique is usually refered to as the “kernel method” in ML theory.
- Inspired from the paper: “Improving the robustness of online agglomerative clustering method based on kernel-induce distance measures”. By Daoqiang Zhang, Songcan Chen, Keren Tan.
- Just the same principle as an online K-means in N dimensions.
- Stream the data. For each new data point:
- Find the closest cluster
- Assign the new data point to this cluser
- Update the cluster centroid accordingly: new_centroid = old_centroid + (new_datapoint - old_centroid) / cluster_size
3. Agglomerative Clustering
- Inspired from the paper: “An on-line agglomerative clustering method for non-stationary data”. By I. D. Guedalia, M. London, M. Werman.
You can find the algo on my GitHub along with other ML projects.