الفهرس 
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Abstract
Clustering is a data mining technique which helps in grouping or making
clusters of data having similar values of some of the data attributes.
Clustering can be used in various elds like in health sector for grouping
patients with similar symptoms of the disease, in banking sector to group
customers who have dues in their credit card payments, in market analysis
to identify the customers having similar buying patterns.
One of the famous clustering algorithm is K-means. K-means is one
of the simplest unsupervised learning algorithms known for its speed and
simplicity. However, this algorithm su ers from two major limitations.
First, the clusters produced are sensitive to the selection of initial centroids
(cluster centers). Second, the algorithm requires number of clusters as
input. This sometimes needs domain speci c and if the user is not domain
expert then many problems can be occurred.
As mentioned earlier, the output of K-means algorithm highly depends
upon the selection of initial cluster centers. Because initial cluster centers
are chosen randomly. Consequently K-means algorithm does not guarantee