High Dimensional Data & High Speed Data Streams – A Survey

Abstract

Clustering is used to grouping objects from the large database. Each group, called cluster, consists of objects that are similar between themselves and dissimilar to objects of other groups. It is a high dimension of the dataset, arbitrary shapes of clusters, scalability, input parameter, domain knowledge and noisy data. Large number of clustering algorithms had been proposed till date to address these challenges. There do not exist a single algorithm which can adequately handle all sorts of requirement. In this paper, we have discussed in K-means Clustering algorithm and Agglomerative clustering algorithm.

Keywords: K-means algorithm, agglomerative algorithm, scalability, High dimensional data streams, outliers.