Create JOSS submission material

Author: lmcinnes

paper/paper.md

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+---
+  title: 'hdbscan: Hierarchical density based clustering'
+  tags:
+    - clustering
+    - unsupervised learning
+    - machine learning
+  authors:
+   - name: Leland McInnes
+     orcid: 0000-0000-0000-1234
+     affiliation: 1
+   - name: John Healy
+     affiliation: 1
+   - name: Steve Astels
+     affiliation: 2
+  affiliations:
+   - name: Tutte Institute for Mathematics and Computing
+     index: 1
+   - name: Shopify
+     index: 2
+  date: 26 February 2017
+  bibliography: paper.bib
+  ---
+
+  # Summary
+
+  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. The library also includes support for Robust Single Linkage clustering,
+  GLOSH outlier detection, and tools for visualizing and exploring cluster structures.
+  Finally support for prediction and soft clustering is also available.
+
+  -![Example clusterign results.](hdbscan_clustering_result.png)
+  -![Hierarchical tree structure.](hdbscan_condensed_tree.png)
+
+  # References
+