Traditional Machine learning Algorithms. (Under Development)
Currently Available algorithms :
- K-means clustering
According to wikipedia, k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
The problem is computationally difficult (NP-hard); however, efficient heuristic algorithms converge quickly to a local optimum.
Package Documentation and examples are under development. You can find basic documentation inside source files.