Paper Details

Clustering, in Data Mining is useful for discovering groups and identifying interesting distributions inunderlying data. Traditional data clustering algorithms either favor clusters with special shapes and similar sizes, or arevery delicate in the presence of outliers. Nowadays most widely studied problem is identification of clusters in a largedataset. Hierarchical Clustering is the process of forming a maximal collection of subsets of objects (called clusters),with the property that any two clusters are either disjoint or nested. Hierarchical clustering combine data objects intoclusters, those clusters into larger clusters, and so forth, creates a hierarchy of clusters, which may represent a treestructure called a dendrogram. Agglomerative clustering is a most flexible method and it is also used for clustering thelarge dataset, there is no need of the number of clusters as an input. In this paper we have introduced solution fordecreasing time complexity of clustering algorithms by combining approaches of two different algorithms from whichone is good in accuracy and other is fast that is helpful for information retrieval from large data.

Mandani Kashmira, Prof.Hemani Shah, “ANALYSIS OF HIERARCHICAL CLUSTERING ALGORITHM TO HANDLE LARGE DATASET”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 1, Issue 11, pp. 286-293, November 2014.