A Recent Overview of Some Hierarchical Clustering Techniques
Journal Title: International Journal of engineering Research and Applications - Year 2018, Vol 8, Issue 5
Abstract
The process of grouping a set of objects into classes of similar objects is called clustering. A cluster is a collection of data objects that are similar to one another within the same cluster and are dissimilar to the objects in other clusters. A cluster of data objects can be treated collectively as one group and so may be considered as a form of data compression. The choice of clustering algorithm depends both on the type of data available and on the particular purpose of the application. Hierarchical clustering method works by grouping data objects into a tree of clusters. Hierarchical clustering methods can be further classified as either agglomerative or divisive, depending on whether the hierarchical decomposition is formed in a bottom-up or top-down fashion. In this paper we proposed a comparative study of some hierarchical clustering.
Authors and Affiliations
Kirti Sharma, Hemant Verma
Keywords
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- EP ID EP394289
- DOI 10.9790/9622-0805054750.
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