Coded by: Benjamin M. Sainz-Tinajero.

Source code of MOCLE [1],[2], a state-of-the-art Pareto-based evolutionary clustering algorithm by Katti Faceli, Marcilio C.P. de Souto, Daniel S.A. de Araújo, and André C.P.L.F. de Carvalho. The algorithm is based upon the search strategy of NSGA-II, and for this implementation we used the source code available in [3]. MOCLE starts by creating an initial population with conceptually diverse clustering algorithms using varying hyper-parameters and cluster numbers in the range of k to 2k. The initial population is constituted using k-means, Single and Average link Agglomerative Clustering from the Scikit-learn library [4], and Shared Nearest Neighbors clustering method with diverse parameters [5]. The crossover operator for recombination selected randomly the number of clusters of the resulting child from the cluster interval of the parents and is performed by Strehl and Ghosh’s Meta-clustering Algorithm using Kultzak’s version [6], avoiding clones. MOCLE returns a set of solutions representing each region of the Pareto front obtained from the optimization of two complementary objective functions to be minimized: deviation and connectivity.

MOCLE is available in this repository in a parallelized Python implementation.

This implementation requires one mandatory file to perform clustering. A .csv file named iris_X.csv, for instance, must contain the features of a dataset with no header, one column per attribute and one row per object. A second optional file could contain ground truth labels in case you're running a benchmark and need to compute the Adjusted RAND Index of a solution against a reference mask or partition. This second file must be named iris_y.csv (for this example) and must have one column with the same number of objects as the iris_X.csv file (no header), placing each object into one group. Our algorithm automatically searches for both of this files in the \data path and computes the Adjusted RAND Index only if it finds ground truth labels in a file as mentioned before. We include 40 publicly available datasets complying with our implementation's required data format.

data: a string with the name of the dataset to be retrieved without the _X.csv or _y.csv suffixes.
n_clusters: integer with the number of required clusters. As an alternative, setting this argument as 'auto' will set the number of clusters found in the ground truth file as n_clusters (this feature only works if there is a _y.csv ground truth file in the \data directory).
runs (default = 10): independent runs of the algorithm.
max_gens (default = 50): maximum generations of the evolutionary process.

For more information on the hyper-parameters and their influence in the evolutionary process, we refer the user to the article in Ref.[1].

Open your preferred Python interface and follow these commands to generate a clustering using MOCLE. We will continue using the iris dataset as an example.

>>> from gen import *
>>> run_mocle(data='iris', n_clusters=3, runs=10, max_gens=50)

Running these commands will execute MOCLE using the iris dataset's features with 3 clusters, 50 generations, and 10 independent runs, and will compute the Adjusted RAND Index between the solutions and the reference labels in the iris_y.csv file. A .csv file with the clustering and the results is stored in the /mocle-out path.

An example.py file is provided with this example for a more straight-forward approach to using the algorithm.

Important: You will need to have previously installed some basic data science packages such as NumPy, Pandas, Metis, Tables, and Scikit-learn.

I hope our implementation to this great method is useful for your clustering tasks,

Benjamin
LinkedIn: https://www.linkedin.com/in/benjaminmariosainztinajero/
Email: [email protected], [email protected]

[1] K. Faceli, A. C. De Carvalho, and M. C. De Souto, “Multi-objective clustering ensemble,” International Journal of Hybrid Intelligent Systems, vol. 4, no. 3, pp. 145–156, 2007.
[2] K. Faceli, M. C. de Souto, D. S. de Araujo, and A. C. de Carvalho, “Multi-objective clustering ensemble for gene expression data analysis,” Neurocomputing, vol. 72, no. 13-15, pp. 2763–2774, 2009.
[3] H. A. Khan, “Nsga-ii,” https://github.com/haris989/NSGA-II, 2017.
[4] F.Pedregosa,G.Varoquaux,A.Gramfort,V.Michel,B.Thirion,O.Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Pas- sos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit- learn: Machine learning in Python,” Journal of Machine Learning Re- search, vol. 12, pp. 2825–2830, 2011.
[5] A. Espin, “Snn-clustering,” https://github.com/albert-espin/snn-clustering, 2019.
[6] A. Kultzak, “Mcla,” https://github.com/kultzak/MCLA, 2019.