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CCCG -- Constrained Clustering using Column Generation What is it? ----------- The problem of clustering in presence of additional constraints has been extensively studied already, and a number of efficient methods to solve these problems (approximately) have been developed. However, these methods are often limited to only a number of specific constraints. CCCG is an attempt to build a more general framework for constrained clustering. It is based on an integer linear programming formulation of clustering problem, and hence obtains an exact solution for this problem. The clustering criterion used in CCCG is similar to that of k-means algorithm, namely to minimize the sum of squared distances from cluster centers. Installation ------------ CCCG depends on Boost C++ libraries, which could be obtained from: <http://www.boost.org/> CCCG also depends on SCIP which is a Mixed Integer programming solver. CCCG has been tested with versions 3.0.1 and 3.1.0. You can obtain SCIP for free from here: <http://scip.zib.de/> We recommend that you install not just the SCIP MIP solver, but rather the whole SCIP optimization suite, which also contains LP solver SOPLEX. SCIP will need such an LP solver for solving the LP relaxations. Alternatively, you could only install SCIP, and later configure it to use your favorite LP solver. Once you have installed SCIP, define the environment variable SCIP_HOME such that it points to the directory where you have installed SCIP. For example: $ export SCIP_HOME=/usr/local/scipoptsuite-3.0.1/scip-3.0.1 Then go to the CCCG home directory and run: $ make depend $ make There are a number of example datasets and example sets of constraints in /examples directory. To verify that CCCG is working correctly, you could run it on these examples. Perhaps the easiest way to do so is to go to the CCCG home directory and run $ make test Usage ----- Like many other clustering algorithms, CCCG assumes that the number of clusters are specified in advance by user. So the minimum input for CCCG are information about objects, and the number of clusters. After going to the CCCG home directory, one could run the following command: $ ./bin/cccg -d <DATA> -k <#CLUSTERS> In the above command, DATA is a file in which the dimensions of data objects are stroed, and #CLUSTERS is the number of clusters. If solving a problem takes too long, you can opt for the best solution found within a time limit. This can be done by using flag -x: $ ./bin/cccg -d <DATA> -k <#CLUSTERS> -x <TIME> In the above command, TIME is the timeout in seconds. To see if any solution has been found within the time limit and whether it is the optimal solution or not, you should check the value of "Primal Bound" and "SCIP Status" (reported near the end of output). To have CCCG consider constraints when generating the optimal clustering, they should be specified in an input file which is communicated to CCCG code using flag -n: $ ./bin/cccg -d <DATA> -k <#CLUSTERS> -n <FILE> In the input constraint file, the Must-Link (ML) and Can-Not-Link (CL) constraints are specified in the usual way: A pair of must-linked objects are followed by +1, and a pair of can-not-linked objects are followed by -1. For an example of constraint file, look into the /examples directory in CCCG home directory. There are several other options that could be used with CCCG. For a list of example commands using these options, look into the /examples directory. Some of these options are described below: * The efficiency of CCCG could be improved by providing an initial clustering. This could be done by using the flag -i: $ ./bin/cccg -d <DATA> -k <#CLUSTERS> -i <INIT> * Instead of specifying a single clustering as an initial solution for CCCG, one could alternatively specify a directory containing several clusterings. In this case, CCCG code will use collects all clusters from all these clusterings, and will add each and every one of them as a column to the problem. This could be done by using the flag -r: $ ./bin/cccg -d <DATA> -k <#CLUSTERS> -r <DIR> * In each call to subproblem-solver, several columns with negative reduced cost are found. One could decide all these columns or only the most negative one. The default behavior is to do the latter. If one prefers to add all columns, this could be done via flag -a: $ ./bin/cccg -d <DATA> -k <#CLUSTERS> -a For a list of all available options, run ./bin/cccg with no arguments. Availability ------------ CCCG software source can be found on the CCCG page under <http://dtai.cs.kuleuven.be/CP4IM/cccg/> Authors ------- Behrouz Babaki Tias Guns Siegfried Nijssen Licensing --------- Please see the file named LICENSE. Publications ------------ Babaki, B., Guns, T., & Nijjsen, S. (2014) Constrained Clustering using Column Generation. Eleventh International Conference on Integration of Artificial Intelligence (AI) and Operations Research (OR) techniques in Constraint Programming (CPAIOR 2014) Acknowledgements ---------------- We are thankful to Khanh-Chuong Duong and Mohadeseh Ganji for their feedback. Contact ------- For questions, bug reports, and any other matters, you could contact Behrouz Babaki via this email address: <Behrouz.Babaki@cs.kuleuven.be>