In a previous paper [1] the use of the concept of fuzzy sets in clustering was proposed. The
convenience of fuzzy clustering over conventional representation was then stressed.
Assigning each point a degree of belongingness to each cluster provides a way of
characterizing bridges, strays, and undetermined points. This is especially useful when
considering scattered data. The classificatory process may be considered as the breakdown
of the probability density function of the original set into the weighted sum of the component�…

In a previous paper [1] the use of the concept of fuzzy sets in clustering was proposed. The
convenience of fuzzy clustering over conventional representation was then stressed.
Assigning each point a degree of belongingness to each cluster provides a way of
characterizing bridges, strays, and undetermined points. This is especially useful when
considering scattered data. The classificatory process may be considered as the breakdown
of the probability density function of the original set into the weighted sum of the component�…