Identification of Valid Clusters for Datasets Whose Number of Clusters are Unknown

Provided by: International Journal of Engineering and Advanced Technology (IJEAT)
Topic: Data Management
Format: PDF
The true use of clustering is not exploited properly as humans try to cluster datasets whose class labels are already known. In order to make best use of clustering, an attempt has been made in this work to find a mechanism to identify the number of clusters in the datasets whose class labels are unknown. The cluster validity techniques like Dunn's index, Davies-Bouldin index, Silhouette index, C index, Goodman-Kruskal index, etc. have been used to validate the number of clusters generated.

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