sparsehc: Memory-efficient hierarchical clustering

Compile:

	From Eclipse: Add the whole downloaded project to workspace and build
	From Linux terminal: Go to the Release folder and type "make"
	The executable file is Release/sparsehc

Command:
		
	./sparsehc --matrix=<input_path> --tree=<output_path> --size=<number_of_data_points>
	--threshold=<distance_threshold> --linkage=<linkage_type>

Parameters:
	
	--matrix=%s	Path to the input matrix file <required>
	--tree=%s 	Path to the output tree file <required>
	--size=%u	Number of data points <required>
	--dim=%u	Dimension of each point <required>
	--threshold=%f	The maximum distance value in the input distance matrix <required>
	--linkage=%s	The linkage type: average, complete or single <required>	
		
Example: 

	./sparsehc --matrix=/Data/sparse_matrix.dat --tree=/Data/average_tree.txt --size=100 --threshold=0.5 --linkage=average
	
Data: 

	To randomly generate pairwise Euclidean distance matrices of different sizes and dimensions:
	https://bitbucket.org/ngthuydiem/simmat