# Difference in meaning between Soft Clustering and Multi-classification techniques

I would like to know the difference in meaning, approach and concept between:

Clustering similar objects into more than one cluster (soft clustering) where objects can be found in more than one cluster, and soft classification where objects can be labeled into more than one class.

For clustering I would like to cluster my data into predefined clusters by used K-means and for classification also there are predefined classes.

In other words:

cluster 1 for example combine ( object 1 , obejct 2 ..etc )
cluster 2 for example combine ( object 1 , obejct 5 ..etc )
and
Class A = for example assign to ( object 1,object 4, etc. )
Class b = for example assign to ( object 1,object 7, etc. )


Peters et al., 2013, where authors compared $k$-means to fuzzy $c$-means and rough $k$-means as important representatives of soft clustering.