Cluster analysis or clustering is the assignment of a set of observations into subsets (called clusters) so that observations in the same cluster are similar in some sense. Clustering is a method of unsupervised learning, and a common technique for statistical data analysis used in many fields, including machine learning, data mining, pattern recognition, image analysis, information retrieval, and bioinformatic.
For the manufacturing industry, the most important criteria was improving responsiveness, followed by staffing, reducing energy consumption, reducing costs and controlling server growth. Cost reduction was perceived as the most important criteria to the financial industry, followed by staffing, controlling storage growth, and adding capacity.
The k-means algorithm assigns each point to the cluster whose centre (also called centroid) is nearest. The centre is the average of all the points in the cluster — that is, its coordinates are the arithmetic mean for each dimension separately over all the points in the cluster.
In k-means clustering methods, it is often requires several analysis before the number of clusters can be determined.
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Raghavendra Nitturkar (13030)
Operations Group 1
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