k_ means method

Clustering is the classification of objects into different
groups, or more precisely, the partitioning of a data set
into subsets (clusters), so that the data in each subset
(ideally) share some common trait - often according to
some defined distance measure.

Hierarchical algorithms: these find successive clusters
using previously established clusters.
1.Agglomerative ("bottom-up"): Agglomerative
algorithms begin with each element as a separate cluster
and merge them into successively larger clusters.
2. Divisive ("top-down"): Divisive algorithms begin with
the whole set and proceed to divide it into successively
smaller clusters.
2. Partitional clustering: Partitional algorithms determine all
clusters at once. They include:
? K-means and derivatives
? Fuzzy c-means clustering
? QT clustering algorithm

Step 1: Begin with a decision on the value of k =
number of clusters .
} Step 2: Put any initial partition that classifies the
data into k clusters. You may assign the training samples
randomly,or systematically as the following:
1.Take the first k training sample as single-element
clusters
2. Assign each of the remaining (N-k) training
sample to the cluster with the nearest centroid. After
each assignment, recompute the centroid of the gaining
cluster.