Semi-supervised learning with a growing self-organizing map (GSOM) is commonly used to cope with the machine

learning problems. The performance of semi-supervised GSOM is associated with the structure of clustering layer, the activation...

over the last few years, manifold clustering has attracted considerable interest in high-dimensional data clustering. However achieving accurate clustering results that match user desires and data structure is still an ...

Semi-supervised clustering tries to surpass the limits of unsupervised clustering using extra information contained in occasional labeled data points. However, providing such labeled samples is not always possible or easy in real world applications...

. Semi-supervised learning algorithms may be used as a proper solution in these situations, where E-neighborhood or k nearest neighborhood graphs are employed to build a similarity graph. These graphs, on one hand, have a high degree of sensitivity...

Semi-Supervised Growing Self Organizing Map (SSGSOM) is one of the best methods for online classification with partial labeled data. Many parameters can affect the performance of this method. The structure of GSOM network, activation degree...

of the problems in the real world have numerous data.

Thereforeassigning labels to every data points in these problemsare a cumbersome or even

impossible matter. Semi-supervised learning is one approach to overcome these types of...