… algorithms that can be used for clustering in large datasets. The comparative analysis of
available clustering algorithms is … for researchers in the large database clustering domain. …

… old data. Most existing clustering algorithms require multiple data scans to achieve convergence
[4], … Algorithms to cluster spatial data have usually been based on standard heirarchical …

… and recent clustering algorithms, used to perform data clustering on large datasets. We
have also discussed the problem of cluster formulation in high-dimensional data, and the …

… limited performance in clustering larger datasets, yielding … of clustering algorithms on static
data. Nevertheless, when analyzing data, it is important to take into account whether the data …

… -means and k-modes cluster algorithms based on the design … show their utility in cluster
stability and sensitivity analysis … based algorithm was increased by a factor of 10 for large data …

… large data volumes and how to analyze the relevant data to produce valuable information. …
In this section advancements of clustering algorithms for big data analysis in categories that …

… Generally processing of these large data is done by machine learning algorithms and … on
analysing clustering algorithm in big data perspective and discussed clustering algorithms such …

… consists in dividing the large dataset into a number K (predefined by the user) of groups
by using a distance as a measure of similarity, where each group represents a …

… for relatively small data sets. In the recent years, the clustering algorithms have been extended
to efficiently work on large data sets, and some of them even allow the clustering of high-…

… In Section 2, we survey related work on clustering large data sets. We present CURE’s
hierarchical clustering algorithm that uses representative points, in Section 3. In Section 4, we …