- You can run the code directly using the .jar file or the .class executables, or you can access and compile the source code yourself from the "source" folder.

- The algorithm needs the Loandra solver to be installed.

- There needs to be 6 folders called "clauses", "consts", "labels", "logs", "pairs", and "solutions" next to the JAR file.

Parameters:

-d [value]: tree depth (specifying no depth results in flat clustering)
-print: to print the results
-norm: to normalize the features in the dataset
-obj [mode] [eps value]: the clustering objective, "wcm" mode for MD only and "wcm+bcm_p" mode for Pareto objective (optional: add "-Smart" after [eps value] to use smart pairs)
-k [value]: number of clusters
-seed [value]: randomization seed
-mcl_c n[value]: number of must and cannot-links to be generated based on the size of the dataset (kappa value)
-sto [value]: timeout limit (in minutes)
-path [string]: the path to the loandra solver
-post [string]: a postfix to distinguish the name of the files stored in the six folders

Dataset:
- The name of the dataset should come at the end of the command
- The dataset file should be named "instance_[dataset name]"
- The dataset format is:
	"flags: l" if labels appear at the end of each line or "flags: lb" if they appear at the beginning
	[n] (size of the dataset)
	[f] (number of features)
	[c] (number of labels)
	each line: first the features and then the label, separated by space


Example:

java com.company.Main -norm -print -seed 1234 -post _test -obj wcm+bcm_p 0.1 -smart -k 2 -d 2 -mcl_c n0.5 -sto 30 -path ~/SAT_Project/loandra-master/loandra_static iris