An empirical comparison of Clustering using hierarchical methods and K-means

Data clustering is the process of grouping data elements based on some aspects of relationship between the elements in the group Clustering has many applications such as data firmness, data mining pattern recognition machine learning and there are many different clustering methods. This paper examines the K-means method of clustering and how the selection of primary seeding affects the result. Hierarchical algorithms are used as a base line and it is compared to a data set.