Clustering techniques for unsupervised Learning

In data mining, division of data into groups of similar objects is known as clustering. From a machine learning perspective cluster correspond to hidden patterns, the search for cluster is unsupervised learning, and the resulting system represents a data concept. From a practical point of view clustering plays an outstanding role in data mining application such as scientific data exploration etc. For doing the scientific data exploration we have used solar interplanetary and geomagnetic data (SIGD) of a long period from the years 1965 to 2006. We have applied the agglomerative hierarchical clustering algorithm and k-mean partitioning algorithm on the data and many interesting clusters have been identified and discussed in this paper.