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Hierarchical Clustering- An Efficient Technique of Data mining for Handling Voluminous Data
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 129 - Number 13 |
| Year of Publication: 2015 |
| Authors: Shuhie Aggarwal, Parul Phoghat, Seema Maitrey |
10.5120/ijca2015907081
|
Shuhie Aggarwal, Parul Phoghat, Seema Maitrey . Hierarchical Clustering- An Efficient Technique of Data mining for Handling Voluminous Data. International Journal of Computer Applications. 129, 13 ( November 2015), 31-36. DOI=10.5120/ijca2015907081
Abstract
The objective of data mining is to take out information from large amounts of data and convert it into form that can be used further. It comes with several functionalities, among which Clustering is worked upon in this paper. Clustering is basically an unsupervised learning where the categories in which the data to put is not known priorly. It is a process where we group set of abstract objects into similar objects such that objects in one cluster are highly similar in comparison to each and dissimilar to objects in other clusters. Clustering can be done by different number of methods such as-partitioning based methods, methods based on hierarchy, density based ,grid based ,model based methods and constraint based clustering. In this survey paper review of clustering and its different techniques is done with special focus on Hierarchical clustering. A number of hierarchical clustering methods that have recently been developed are described here, with a goal to provide useful references to fundamental concepts accessible to the broad community of clustering practitioners.
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