Finding the representative in a cluster using correlation clustering

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.

[1]  Peter Šulek,et al.  The impact of genetic algorithm parameters on the optimization of hydro-thermal coordination , 2016 .

[2]  Anindya Bhattacharya,et al.  Divisive Correlation Clustering Algorithm (DCCA) for grouping of genes: detecting varying patterns in expression profiles , 2008, Bioinform..

[3]  Sebastian Nowozin,et al.  Higher-Order Correlation Clustering for Image Segmentation , 2011, NIPS.

[4]  Martin Aigner,et al.  Enumeration via ballot numbers , 2008, Discret. Math..

[5]  András Horváth,et al.  ERROR HANDLING TECHNIQUES OF GENETIC ALGORITHMS IN PARALLEL COMPUTING ENVIRONMENT , 2008 .

[6]  Z. Néda,et al.  Phase transition in an optimal clusterization model , 2006 .

[7]  Tamás Mihálydeák,et al.  Rough Clustering Generated by Correlation Clustering , 2013, RSFDGrC.

[8]  David E. Goldberg,et al.  Genetic algorithms and Machine Learning , 1988, Machine Learning.

[9]  Tamás Mihálydeák,et al.  Rough Classification Based on Correlation Clustering , 2014, RSKT.

[10]  Anthony Wirth,et al.  Correlation Clustering , 2010, Encyclopedia of Machine Learning and Data Mining.

[11]  Jiming Liu,et al.  Community Mining from Signed Social Networks , 2007, IEEE Transactions on Knowledge and Data Engineering.

[12]  Hila Becker.  A Survey of Correlation Clustering , 2005 .

[13]  Zhikui Chen,et al.  A clustering approximation mechanism based on data spatial correlation in wireless sensor networks , 2010, 2010 Wireless Telecommunications Symposium (WTS).