Correlation clustering is a widely used technique in data mining. The clusters contain objects, which are typically similar to each other and different from objects from other groups. It can be an interesting task to find the member, which is the most similar to the others for each group. These objects can be called representatives. In this paper, a possible way to find these representatives are shown and software to test the method is also provided.
Bansal N. , Blum A., Chawla S. Correlation clustering, Machine Learning, Vol. 56, No. 1-3, 2004, pp. 89–113.
Becker H. A survey of correlation clustering, COMS E6998: Advanced Topics in Computational Learning Theory, 2005, pp. 1–10.
Zimek A. Correlation clustering, ACM SIGKDD Explorations Newsletter, Vol. 11, No. 1, 2009, pp. 53‒54.
Kim S. , Nowozin S., Kohli P., Yoo C. D. Higher-order correlation clustering for image segmentation, Advances in Neural Information Processing Systems, Vol. 24, 2011, pp. 1530–1538.
Bhattacharya A. , De R. K. Divisive correlation clustering algorithm (dcca) for grouping of genes: detecting varying patterns in expression profiles, Bioinformatics, Vol. 24, No. 11, 2008, pp. 1359‒1366.
Yang B. , Cheung W. K., Liu J. Community mining from signed social networks, IEEE Transactions on Knowledge and Data Engineering, Vol. 19, No. 10, 2007, pp. 1333‒1348.
Chen Z. , Yang S., Li L., Xie Z. A clustering approximation mechanism based on data spatial correlation in wireless sensor networks, IEEE Wireless Telecommunications Symposium, Tampa, FL, USA, 21-23 April 2010, pp. 1–7.
Neda Z. , Florian R., Ravasz M., Libál A., Györgyi G. Phase transition in an optimal clusterization model, Physica A, Statistical Mechanics and its Applications, Vol. 362, No. 2, 2006, pp. 357‒368.
Aszalos L. , Mihálydeák T. Rough clustering generated by correlation clustering, In: Ciucci D., Inuiguchi M., Yao Y., Ślęzak D., Wang G. (Eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, Lecture Notes in Computer Science, Vol. 8170, Springer, Berlin, Heidelberg 2013, pp. 315–324.
Aszalos L. , Mihálydeák T. Rough classification based on correlation clustering, In: Miao D., Pedrycz W., Ślȩzak D., Peters G., Hu Q., Wang R. (Eds) Rough Sets and Knowledge Technology, Lecture Notes in Computer Science, Vol. 8818. Springer, 2014, pp. 399–410.
Aigner M. Enumeration via ballot numbers, Discrete Mathematics, Vol. 308, No. 12, 2008, pp. 2544‒2563.
Goldberg D. E. , Holland J. H. Genetic algorithms and machine learning, Machine Learning, Vol. 3, No. 2, 1988, pp. 95‒99.
Kinczer T. Šulek P. The impact of genetic algorithm parameters on the optimization of hydro-thermal coordination, Pollack Periodica, Vol. 11, No. 2, 2016, pp. 113‒123.
Hatwagner M. , Horvath A. Error handling techniques of genetic algorithms in parallel computing environment, Pollack Periodica, Vol. 3, No. 2, 2008, pp. 3‒14.
GitHub, https://github.com/lordimp88/representative (last visited 27 December 2017)