How the K-mean Cluster work
Step 1. Begin with a decision 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:
Take the first k training sample as single-element Clusters
Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster.
Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the Clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample.
Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.
The last step in training phase is refinement of the Clusters
found above. Although DynamicClustering counters all the
basic k-means disadvantages, setting the intra-cluster similarity
r may require experimentation. Also, a cluster may
have a lot in common with another, i.e., sequences assigned
to it are as close to it as they are to another cluster. There
may also be denser sub-Clusters within the larger ones.
