Research Journal of Applied Sciences, Engineering and Technology 4(10): 1391-1394, 2012
ISSN: 2040-7467
© Maxwell Scientific Organization, 2012
Submitted: January 23, 2012
Accepted: March 02, 2012
Published: May 15, 2012
Implementation of K-Means Clustering in Cloud Computing Environment
A. Mahendiran, N. Saravanan, N. Venkata Subramanian and N. Sairam
School of Computing, SASTRA University, Tanjore, Tamilnadu, India
Abstract: Data mining algorithms are proven algorithms to find hidden useful information from large database.
Cloud Computing is a web-based technology whereby the resources are provided as shared services. The large
volume of business data can be stored in Cloud Data centers with low cost. Both Data Mining techniques and
Cloud Computing helps the business organizations to achieve maximized profit and cut costs in different
possible ways. K-Means clustering algorithm is one of the very popular and high performance clustering
algorithms. The main aim of this work is to implement and deploy K-Means algorithm in Google Cloud using
Google App Engine with Cloud SQL.
Key words: Cloud computing, cloud SQL, cluster, database, dataset, google app engine
INTRODUCTION
Clustering is one of the well-known Data mining
techniques to find useful pattern from a data in a large
database (Fayyad, 1996). These patterns are very useful
for the knowledge workers such as financial analyst,
Manger etc… to take right managerial decisions.
K-Means clustering (MacQueen, 1967) is one of the
most famous clustering algorithms applied in different
types of domains such as Biology and Zoology, Medicine
and Psychiatry, Sociology and Criminology, Geology,
Geography and Remote sensing, Pattern recognition and
Market research, and Education etc… (Julie, 1982) to find
the useful patterns.
Today’s business world is fast and dynamic in nature.
It involves lot of data gathered from different sources.
These data are stored in Data Warehouses. The most
challenging task of the business people is to transform
these data into useful information called knowledge. Data
mining techniques are used to achieve this task.
Cloud Computing offers several benefits to the
business organization to cut the initial investments to
establish infrastructure for storage and compute. Many
business organizations have already started migration of
their business data into cloud data centers. Most often
they need to mine useful information from the data stored
in the cloud data centers with regards to business
decisions. So the main objective of this work is to
incorporate and implement K-Means Data mining
technique into Cloud environment.
K-means algorithm: K-Means algorithm (Pang-Ning
et al., 2006) follows the partitional or nonhierarchical
clustering approach (Jain and Dubes, 1988). It involves
partitioning the given data set into specific number groups
called Clusters (Klosgen and Zytkow, 1996). Each cluster
is associated with a enter point called centroid. Each point
is assigned to a cluster with the closest centroid. The main
drawback of K-Means is the number of clusters must be
known in advance, which is defined by K.
C
C
C
C
C
Select K points as the initial centroids
Repeat
Form K Clusters by assigning all points to closest
centroid
Recompute the centroid of each cluster
Until The centroids don’t change
The Initial centroids will be chosen randomly. The
centroid is nothing but the mean of the points in the
cluster. Euclidean distance is used to measure the
closeness. K-Means generates different clusters in
different runs (Murat et al., 2011).
EXPERIMENTAL METHODOLOGY
We implement the K-Means algorithm in java so we
choose Eclipse IDE for design and development of the
application. To deploy the application in Google, we
download the Google App Engine Plug-In from Google’s
official site. To create Database and table we use Google
Cloud SQL.
Figure 1 shows the components of our application.
There are four important components:
C
C
C
C
Client user interface
Google App Engine
Cloud SQL
Client browser window
Corresponding Author: A. Mahendiran, School of Computing, SASTRA University, Tanjore, Tamilnadu, India
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Res. J. Appl. Sci. Eng. Technol., 4(10): 1391-1394, 2012
Client application
(using eclipse IDE)
Finally, the client browser is used to view the output
clusters.
Client browser
window
Steps for implementation and deployment of K-means in
cloud:
Google app engine
Google cloud SQL
RDBMS
MySQL
Internet
Fig. 1: Architecture of cloud data mining
Client Application is nothing but the user interface
and the K-Means implementation in java that has been
created using Eclipse IDE. The Second major component
is Google App Engine, we chose Google app engine to
deploy our application. The Real time Data set has been
taken from Machine learning Repository, 2012
(archive.ics.uci.edu/ml/datasets.html) for analysis. The
third component Cloud SQL was used for storing the Data
Set. We created database and tables in Cloud SQL.
Step-1: Create Database and table in Cloud SQL
Step-2: Take the real-time Dataset from Machine learning
repository, 2012 and store it in to respective table
Step-3: Write and execute a sample SELECT query and
check whether it works well or not in Cloud SQL
of Google API’s console.
Step-4: Design the User Interface and write code in Java
for K-Means
Step-5: Debug and deploy the application in Google App
Engine Cloud
Step-6: Go to the browser window and view the output
clusters.
EXPERIMENTAL RESULTS
As we stated in the introduction, the main objective
of this work is to implement the K-Means algorithm in
Fig. 2: Output clusters of iris data set
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Res. J. Appl. Sci. Eng. Technol., 4(10): 1391-1394, 2012
Fig. 3: Output clusters of blood transfusion service center data set
Cloud environment. For the experiment we took two data
sets from the well known real world data base “Machine
learning repository, 2012”.
The first data set we used was “Iris Dataset” (Fisher,
1936). It consists of 5 attributes and150 instances. The
attributes are sepal width, sepal length, petal width, petal
length and class label. It has three classes Iris flowers
namely:
C
C
C
Iris setosa
Iris versicolor
Iris virginica
We have created a database called “KMean” and two
tables to store the data sets of iris and Blood Transfusion
Service Center Data Set in Cloud SQL using MySQL. As
one of the properties of K-Mean is that it assumes the
number of clusters K is known in advance.
For the first experiment, we assume that k = 3.
Figure 2 shows the output clusters of Iris dataset obtained
from the experiment. Since the value of K is 3 the
numbers of output clusters are three. The contents of each
cluster are displayed under the name cluster1, cluster2 and
cluster3.
The second data set we used was “Blood Transfusion
Service Center Data Set”. It consists of 5 attributes and
748 instances. We have taken first 200 data for analysis.
As one of the properties of K-Means is that it assumes the
number of clusters K is known in advance. Here we
assume that k = 2. Figure 3 shows the output clusters of
Blood Transfusion Service Center Data Set obtained from
the experiment. Since the value of K is 2, the number of
output clusters was two.
CONCLUSION
It is widely accepted that K-Means algorithm is very
popular clustering algorithm to analyse any real world
problems. K-Means algorithm is more efficient algorithm
for mining large Databases and Cloud computing provides
solution for storing large database with less cost. So, in
this paper, we focused the implementation of K-Means
algorithm in the Cloud environment and the experimental
results shows that it works well in the Cloud.
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