Optimization of K-Means Clustering for Weather Dataset

Clustering is the one of the major data mining technique use to classify or partition the data in different clusters based on distance analysis. There are number of existing clustering algorithms to perform the clustering process. But these clustering algorithms are unsupervised so that sometimes impurities can occur because of the unequal partitions. The impurities can be in terms of cluster size range of data values standard deviation of clustered values etc. Because of this there is the requirement of some supervised control mechanism to derive the effective and accurate results. The presented work is in the same direction to achieve the effectiveness by implementing the dynamic supervision. In the work we have implemented a control mechanism before and after the clustering process.