The program contained in this repository has been used to create the following scientific article:
Kordos M., Czepielik Ł., Blachnik M. (2018) Data Set Partitioning in Evolutionary Instance Selection. In: Yin H., Camacho D., Novais P., Tallón-Ballesteros A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science, vol 11314. Springer, Cham
Implementation of K-Means algorithm based on random cluster initialization. Algorithm adapted to multiple clustering process - a set of data can be divided into a defined number of clusters, each of which newly created clusters can be subdivided again - the process can be repeated.
For graphical visualization of the clustering process, an additional class has been created, which places on the axes of coordinates points located in individual clusters.
