Performance Analysis of Hierarchical Clustering Algorithm

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 proximity according to some defined distance measure. Data clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bio informatics. This paper explains the implementation of agglomerative and divisive clustering algorithms applied on various types of data. The details of the victims of Tsunami in Thailand during the year 2004, was taken as the test data. Visual programming is used for implementation and running time of the algorithms using different linkages (agglomerative) to different types of data are taken for analysis.