Create README.md

README.md

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+# HDBSCAN-CPP
+Fast and Efficient Implementation of HDBSCAN in C++ using STL.
+Authored by:
+Sumedh Basarkod
+Rohan Mohapatra
+--------------------------------------------------------------------------------------------------------------
+
+The Standard Template Library (STL) is a set of C++ template classes to provide common programming 
+data structures and functions such as lists, stacks, arrays, etc. It is a library of container classes, algorithms, and iterators. 
+
+# About HDBSCAN
+HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with Noise. Performs DBSCAN over varying epsilon values and integrates the result to find a clustering that gives the best stability over epsilon. This allows HDBSCAN to find clusters of varying densities (unlike DBSCAN), and be more robust to parameter selection.
+
+In practice this means that HDBSCAN returns a good clustering straight away with little or no parameter tuning -- and the primary parameter, minimum cluster size, is intuitive and easy to select.
+
+HDBSCAN is ideal for exploratory data analysis; it's a fast and robust algorithm that you can trust to return meaningful clusters (if there are any).
+
+Based on the paper:
+> R. Campello, D. Moulavi, and J. Sander, Density-Based Clustering Based on Hierarchical Density Estimates In: Advances in Knowledge Discovery and Data Mining, Springer, pp 160-172. 2013
+
+### How to Run this code?
+
+Clone this project as this contains the library.
+```
+git clone https://github.com/rohanmohapatra/hdbscan-cpp.git
+```
+
+Run the Makefile
+```
+make all clean
+```
+
+Wait for it to complete, this will run the already present example in the Four Prominent Cluster Example Folder. Plot the points and see the clustering.
+To run:
+```
+./main
+```
+
+If you want to use it , have a look at the example and use it.
+
+
+
+### Outlier Detection
+The HDBSCAN clusterer objects also support the GLOSH outlier detection algorithm. After fitting the clusterer to 
+data the outlier scores can be accessed via the `outlierScores_` from the `Hdbscan` Object. The result is a vector of score values,
+one for each data point that was fit. Higher scores represent more outlier like objects. Selecting outliers via upper 
+quantiles is often a good approach.
+
+Based on the papers:
+> R.J.G.B. Campello, D. Moulavi, A. Zimek and J. Sander Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection, ACM Trans. on Knowledge Discovery from Data, Vol 10, 1 (July 2015), 1-51.
+
+## Examples
+```
+#include<iostream>
+#include"../HDBSCAN-CPP/Hdbscan/hdbscan.hpp"
+using namespace std;
+int main() {
+
+	Hdbscan hdbscan("HDBSCANDataset/FourProminentClusterDataset.csv");
+  hdbscan.loadCsv(2);
+	hdbscan.execute(5, 5, "Euclidean");
+	hdbscan.displayResult();
+	cout << "You can access other fields like cluster labels, membership probabilities and outlier scores."<<endl;
+
+	/*Use it like this
+	hdbscan.labels_;
+	hdbscan.membershipProbabilities_;
+	hdbscan.outlierScores_;
+	*/
+	return 0;
+
+}
+```
+
+### Help and Support
+If you have issues, please check the issues on github. Finally, if no solution is available there feel free to open an issue ; 
+the authors will attempt to respond.
+
+### Contributing
+We welcome contributions in any form! Assistance with documentation, is always welcome. 
+To contribute please fork the project make your changes and submit a pull request. 
+We will do our best to work through any issues with you and get your code merged into the master branch.