Updated docs for v0.1.1

Author: PyDataBlog

docs/src/index.md

@@ -13,7 +13,7 @@ Say hello to `ParallelKMeans`!
 
 This package aims to utilize the speed of Julia and parallelization (both CPU & GPU) to offer an extremely fast implementation of the K-Means clustering algorithm and its variations via a friendly interface for practioners.
 
-In short, we hope this package will eventually mature as the "one stop" shop for everything KMeans on both CPUs and GPUs.
+In short, we hope this package will eventually mature as the "one stop" shop for everything K-Means on both CPUs and GPUs.
 
 ## K-Means Algorithm Implementation Notes
 
@@ -51,19 +51,20 @@ git checkout experimental
 ## Features
 
 - Lightening fast implementation of Kmeans clustering algorithm even on a single thread in native Julia.
-- Support for multi-theading implementation of Kmeans clustering algorithm.
+- Support for multi-theading implementation of K-Means clustering algorithm.
 - 'Kmeans++' initialization for faster and better convergence.
-- Modified version of Elkan's Triangle inequality to speed up K-Means algorithm.
+- Implementation of available classic and contemporary variants of the K-Means algorithm.
 
 ## Pending Features
 
 - [X] Implementation of [Hamerly implementation](https://www.researchgate.net/publication/220906984_Making_k-means_Even_Faster).
+- [X] Interface for inclusion in Alan Turing Institute's [MLJModels](https://github.com/alan-turing-institute/MLJModels.jl#who-is-this-repo-for).
 - [ ] Full Implementation of Triangle inequality based on [Elkan - 2003 Using the Triangle Inequality to Accelerate K-Means"](https://www.aaai.org/Papers/ICML/2003/ICML03-022.pdf).
 - [ ] Implementation of [Geometric methods to accelerate k-means algorithm](http://cs.baylor.edu/~hamerly/papers/sdm2016_rysavy_hamerly.pdf).
-- [ ] Support for DataFrame inputs.
+- [ ] Native support for tabular data inputs outside of MLJModels' interface.
 - [ ] Refactoring and finalizaiton of API desgin.
 - [ ] GPU support.
-- [ ] Even faster Kmeans implementation based on current literature.
+- [ ] Even faster Kmeans implementation based on recent literature.
 - [ ] Optimization of code base.
 - [ ] Improved Documentation
 - [ ] More benchmark tests
@@ -175,14 +176,15 @@ ________________________________________________________________________________
 ## Release History
 
 - 0.1.0 Initial release
+- 0.1.1 Added interface for MLJ
 
 ## Contributing
 
 Ultimately, we see this package as potentially the one stop shop for everything related to KMeans algorithm and its speed up variants. We are open to new implementations and ideas from anyone interested in this project.
 
 Detailed contribution guidelines will be added in upcoming releases.
 
-<!--- Insert Contribution Guidelines Below --->
+<!--- TODO: Contribution Guidelines --->
 
 ```@index
 ```