There is a need to scrutinise and retrieve information from data in today's world. Clustering is
an analytical technique which involves dividing data into groups of similar objects. Every …

It has often been asserted that since hierarchical clustering algorithms require pairwise
interobject proximities, the complexity of these clustering procedures is at least O (N 2) …

Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly
finer granularity. Motivated by the fact that most work on hierarchical clustering was based …

The purpose of this correspondence is to indicate that stateof-the-art hierarchical clustering
algorithms have 0 n time complexity and should be referred to in preference to the O (n) …

People are living in a world full of data. Humans are collecting data from many
measurements and observations in their daily works. The sorting of these numerous data is …

This paper discusses the issue of clustering algorithms. Clustering algorithms are important
in many fields of science. Paper provides the basic concepts and presents an …

Clustering is an important data exploration task. A prominent clustering algorithm is
agglomerative hierarchical clustering. Roughly, in each iteration, it merges the closest pair of …

We show that for any data set in any metric space, it is possible to construct a hierarchical
clustering with the guarantee that for every k, the induced k-clustering has cost at most eight …

Dasgupta and Long [7] have shown that it is possible to construct a hierarchical clustering
with the guarantee that for every positive integer k, the induced k-clustering has cost at most …

K-means algorithm is a well known nonhierarchical method for clustering data. The most
important limitations of this algorithm are that:(1) it gives final clusters on the basis of the …