More formatting

Author: lmcinnes

paper/paper.md

@@ -21,18 +21,18 @@ date: 26 February 2017
 bibliography: paper.bib
 ---
 
-  # Summary
+# 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.
+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)
+-![Example clusterign results.](hdbscan_clustering_result.png)
+-![Hierarchical tree structure.](hdbscan_condensed_tree.png)
 
-  # References
+# References