Implementation of Fixed-width clustering algorithm

Question

Dear All,

hope this message finds you well.

I want to ask about the the implementation of the Fixed-width clustering algorithm.

anyone can help me ASAP?


Fixed-width clustering algorithm:
Fixed width clustering creates a set of clusters
of fixed radius (width) w. Here the width w is a parameter to
be specified by the user. First, a data vector is taken and used as
the centroid (center) of the first cluster with radius w. Then for
each subsequent data vector the Euclidean distance between the
centroid of the current clusters and this data vector is computed. If
the distance to the closest cluster center from the data vector is less
than the radius w, the data vector is added into that cluster and the
centroid of that cluster is adjusted to the mean of the data vectors
it contains. If the distance to the closest cluster center is more than
the radius w, then a new cluster is formed with that data vector as
the centroid. This operation produces a set of disjoint, fixed width
(radius of w) clusters in the feature space.

Thanks in advance