Abstract of data. The meaningful is termed here

Abstract — Cloud data Storage is a service
where data is remotely maintained, managed, and backed up. The service allows
the users to store files online, so that they can access them from any location
via the Internet. Cloud
computing, and many users expect that cloud computing will reshape information
technology processes. Huge amount of data is stored in the cloud which needs to
be retrieved efficiently. The retrieval of information from cloud takes a lot
of time as the data is not stored in an organized way. Data mining is thus
important in cloud computing. We can integrate data mining and cloud computing
(Integrated Data Mining and Cloud Computing– IDMCC) which will provide agility
and quick access to the technology. With the cloud computing technology, users
use a variety of devices, including PCs, laptops, smart phones, and PDAs to
access programs, storage, and application-development platforms over the
Internet, via services offered by cloud computing providers. Advantages of the
cloud computing technology include cost savings, high availability, and easy
scalability. Thus in this presented work a survey is introduced for cloud data
storage, and their cluster analysis for utilizing the data into various
business intelligence applications.  This
paper suggests a new model of cluster analysis of data is proposed which provides
the clustering as service.


cloud computing, cloud storage, clustering, types of clustering




Large volume of data is stored in the
cloud environment and needs to be retrieved efficiently. The retrieval of
information from cloud takes a lot of time as the data is not stored in an
organized way.


Data Clustering is a technique of
analysing data and extraction of meaningful patterns from the raw sets of data.
The meaningful is termed here to indicate the patterns or knowledge recovered
from the training samples which is further used to identify the similar pattern
which belongs to the learned pattern. In the data clustering two main kinds of
learning techniques are observed namely supervised learning technique and
unsupervised learning technique. These learning models are used to evaluate
data and create a mathematical model for utilizing to identify the similar data
patterns arrived for classifying them in some pre-fined groups. In supervised learning technique the
data is processed with their class labels and here the class labels are working
as teacher for learning algorithm. On the other hand in unsupervised learning
technique the data not contains the class labels to utilize as the teacher.
Therefore using the similarity and dissimilarity of the input training samples
the data is categorized. Therefore the supervised learning processes are known
as the classification of data and the unsupervised learning techniques are
supporting the cluster analysis of data. In this presented work the unlabelled
data is used for analysis therefore the data analysis technique is used as the
cluster analysis. Clustering is the unsupervised classification of patterns or
input samples. That can used classify observations, data items, or feature
vectors into groups. These groups are in data mining is known as the cluster
analysis of data. In the case of clustering, the problem is to group a given
collection of unlabelled patterns into meaningful clusters. In a sense, labels
are associated with clusters also, but these category labels are data driven; that
is, they are obtained solely from the data.1.1
Clustering technique background Clustering is a most popular data mining
technique used to find useful unknown pattern from data in large repository.
Clustering is Grouping of data into different clusters such that elements
belongs to same cluster are most similar while elements belongs to different
cluster are dissimilar. Basically Clustering methods are divided into two broad
categories. i) Hard clustering ii) Soft Clustering. In Hard Clustering, each
document can belong to only one Cluster. Hard Clustering is also known as
exclusive clustering. In Soft Clustering Same document can belong to more than
one group. It is also known as Overlapping Cluster technique. This section provides the overview of
the introduction of data clustering and the selected domain for study in data
storage. In the next section the different kinds of clustering algorithms are learned for
understanding the technique behind the cluster analysis. 1.2
Types of clustering technique There are a significant amount of
clustering algorithms and methods are available some essential techniques are

MethodIn this clustering approach the n
numbers of data or objects are provided, and k number of partitions are
required from the data but the number of partition is such that k?n. This means
the partitioning algorithm will generate k partitions satisfying below
condition: a. Each group have minimum one object. b. Each object should be a
member of exactly one group.  1.2.1       
MethodsHierarchical method generates
hierarchically manner of clusters organization. That can be achieved using the
following manner:

follows the bottom-up approach. Firstly, it generates separate group for each
object of data. Next, it merges these groups on the basis of closer
similarities. This process is repeated till the entire crowd of groups are not
combined in a single or until the termination condition holds. It
follows the top down approach. Process starts with a single cluster having all
data objects. Then, it continues splitting the bigger clusters into smaller
ones. This process continues until the termination condition holds. This method
is inflexible that is after merge or split is finished, It can never be
technique uses the perception of density. The main design is to keep expanding
the cluster until the density of neighbourhood reaches certain threshold i.e.
within a given cluster, the radial span of a cluster must possess certain
number of points for each data points.This
method quantizes the object space into a large no. of cells which together
nurture a grid. The method having the flowing advantages:

benefit the method provides is its fast processing.

only dependability is relying upon the no. of cells in object space.In
Model-based scheme, a model can be conjectured for every cluster along with
that; it then identifies data fitting best into that model. This method
supplies a means to automatically reveal number of clusters derived from the
standard statistics, considering outlier or noise. As a result, it creates
robust clustering methods.It performs clustering on the basis of constraints
either application oriented or user oriented. These constraints are actually
the prospect or properties of the desired clustering results. These constraints
make communication with the clustering process easy.