Improvement in Hierarchical Clustering to Reduce Execution Time and Improve Accuracy
This work presents an overview of the hierarchal clustering algorithm & various enhanced variations done on hierarchal clustering algorithm. Hierarchal is the basic algorithm used for discovering clusters within a dataset. The initial point selection effects on the results of the algorithm, both in the number of clusters found and their centroids. Methods to enhance the k-means clustering algorithm are discussed. With the help of these methods efficiency, accuracy, performance and computational time are improved. Some enhanced variations improve the efficiency and accuracy of algorithm. Basically in all the methods the main aim is to reduce the number of iterations which will decrease the computational time. Studies shows that hierarchal algorithm in clustering is widely used technique. Various enhancements done on hierarchal are collected, so by using these enhancements one can build a new hybrid algorithm which will be more efficient, accurate and less time consuming than the previous work.
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