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International Journal of Advanced Research in Technology, Engineering and Science (A Bimonthly Open Access Online 
Journal) Volume1, Issue2, Sept-Oct, 2014.ISSN:2349-7173(Online) 
Comparison of Different Clustering Algorithms 
using WEKA Tool 
Priya Kakkar1, Anshu Parashar2 
______________________________________________ 
Abstract: 
Data Mining is a process of extracting useful information 
from a large dataset and Clustering is one of important 
technique in data mining process, whose main purpose is to 
group data of similar types into clusters and finding a 
structure among unlabelled data. In this paper we have 
taken four different clustering algorithm i.e. K-Mean 
algorithm, Hierarchical algorithm, Density based algorithm, 
EM algorithm. All these algorithms are applied on data of 
egit software repositories and depends or dependent classes. 
In this paper to compare and analyze these four algorithms 
with respect to time to build a model, cluster instances, 
squared errors and log likelihood by using Weka tool. 
_________________________________________________ 
Keywords: Data Mining, Clustering, K-mean, Weka tool, 
DBSCAN 
__________________________________________________ 
I.INTRODUCTION 
Data mining is a field used to find out the data hidden in your 
clusters of data or massive set of data. Data mining is an 
important tool to convert the data into information. It is used 
in a different field of practices, such as marketing, fraud 
detection and scientific discovery. Data mining is the also 
used for extracting patterns from data. It can be used to 
uncover patterns in data but is often carried out only on 
sample of data. The mining process will be ineffective if the 
samples are not good representation of the larger body of the 
data. The discovery of a particular pattern in a particular set of 
data does not necessarily mean that pattern is found elsewhere 
in the larger data from which that sample was drawn. An 
important part of the method is the verification and validation 
of patterns on other samples of data. A primary reason for 
using data mining is to assist in the analysis of collection of 
observations of behavior. Data mining is the analysis step of 
the "Knowledge Discovery in Databases" process and is the 
process that attempts to discover patterns from large data sets. 
The main aim of the data mining process is to extract 
information from a data set and transform it into an 
understandable format for further use. 
________________________________________________ 
First Author’s Name: Priya Kakkar, Department of Computer Science & 
Engineering, HCTM Technical Campus, Kaithal, India. 
Second Author’s Name: Anshu Parashar, Department of Computer Science 
& Engineering, HCTM Technical Campus, Kaithal, India. 
__________________________________________________________ 
Clustering is the task of assigning a set of objects into groups 
(called clusters) so that the objects in the same cluster are 
more similar to each other than to those in other clusters. 
A cluster is therefore a collection of objects which are 
“similar” between them and are “dissimilar” to the objects 
belonging to other clusters. Clustering is a common technique 
used for statistical data analysis in many fields like machine 
learning, pattern recognition, image analysis, information 
retrieval, and bioinformatics. 
II.CLUSTERING METHODS 
The goal of clustering is to organize objects which are related 
to each other or have similar characteristics. Clustering groups 
similar objects (item) into same group. We use different 
methods for clustering. 
· Partitioning clustering 
The partitioning method uses a set of M clusters and each 
object belongs to one cluster. Each cluster can be represented 
by a centroid or a cluster representative; that is a description 
of all the objects contained in a cluster. This description will 
depend on the type of the object which is clustered. In real-valued 
data the arithmetic mean of the attribute vectors for all 
objects within a cluster provides an appropriate representative 
while alternative types of centroid may be required in other 
cases. If the number of the clusters is large then centroid can 
be further clustered which produces hierarchy within a 
dataset. 
· Hierarchical clustering 
Flat clustering is efficient and conceptually simple but it has a 
number of drawbacks. The algorithms require a pre-specified 
number of clusters as input and are nondeterministic. 
Hierarchical clustering outputs a hierarchical structure that is 
more informative than the unstructured set of clusters formed 
by flat clustering. Hierarchical clustering also does not need to 
specify the number of clusters in advance. In hierarchical 
clustering clusters are created either by top-down or bottom-up 
fashion by recursive partitioning. Hierarchical clustering 
are of two types: - Hierarchical Agglomerative methods, 
Hierarchical Divisive clustering. 
· Density based clustering 
Density-based clustering algorithms try to find clusters based 
on density of data points in a region. The key idea behind 
density-based clustering is that for each instance of a cluster 
the neighborhood of a given radius (Eps) has to contain at 
least a minimum number of instances (MinPts). Density based 
clustering is based on probability distribution and points from 
All Rights Reserved © 2014 IJARTES Visit: www.ijartes.org Page 20
International Journal of Advanced Research in Technology, Engineering and Science (A Bimonthly Open Access Online 
Journal) Volume1, Issue2, Sept-Oct, 2014.ISSN:2349-7173(Online) 
one distribution are assumed to be part of one cluster. This 
method identifies the clusters and their parameters. 
IV.VARIOUS CLUSTARING ALGORITHMS 
· k-mean clustering 
K-means is a widely used partition based clustering method 
because it can be easily implemented and most efficient one in 
terms of the execution time. k-mean clustering group items 
into k groups. This grouping is done on the basis of 
minimizing the sum of squared distances between items and 
the corresponding centroid. A centroid is "center of mass of a 
geometric object of uniform density". 
K-Means Algorithm: In k-mean algorithm each cluster’s 
center is represented by mean value of objects in the cluster. 
Input: k: the number of clusters. 
D: data set containing n objects. 
Output: A set of k clusters. 
Method: 
1. Arbitrarily choose k objects from D as the initial cluster 
centers. 
2. Repeat. 
3. Reassign each object to the cluster to which the object is 
most similar based on the mean value of the objects in the 
cluster. 
4. Update the cluster means. 
5. until no change. 
· EM algorithm 
In cases where the equations cannot be solved directly we use 
a special algorithm known as The EM algorithm. EM stands 
for Expectation and Maximization which is part of data 
mining tools.The EM algorithm is used to find most likelihood 
parameters in a model. These models Contains latent variable 
and use likelihood functions in addition to unknown 
parameters and known data observations. It contains either 
missing value among the data, or the model can be simplified 
by assuming the existence of additional unobserved data 
points. To find out solutions it requires taking derivatives of 
likelihood functions with respect to all unknown values. The 
result is typically a set of interlocking equations in which the 
solution to the parameters requires the values of the latent 
variables and vice-versa, but substituting one set of equations 
into the other produces an unsolvable equation. EM algorithm 
pick arbitrarily values for one of sets and use these values to 
estimate the second set then use these values to estimate first 
set and this will continue until the resulting values converge to 
fixed points. 
· Density-based spatial clustering of applications with 
noise (DBSCAN) Algorithm 
Density based spatial clustering of application with noise is 
one of Density based algorithm. It separates data points into 
three parts: Core points (points that are at the interior of a 
cluster), Border points (points which fall within neighborhood 
of core point) and Noise points (point that is not a core point 
or a border point).DBSCAN starts with an arbitrary instance 
(p) in data set (D) and finds all values of D within Eps and 
MinPts. The algorithm uses a spatial data structure to place 
points within Eps from the core points of the clusters. It starts 
with an arbitrary starting point that has not been visited and 
point’s Eps-neighborhood is found out and if it contains 
sufficiently many points, a cluster is started. Otherwise, point 
is recognized as noise. 
This point might later be found within Eps-environment of a 
different point and hence it’s to made part of a cluster. If a 
point is found a dense part of a cluster then its Eps-neighborhood 
is also part of that cluster. Hence, all points 
which are found within the Eps-neighborhood are also added 
like their own Eps-neighborhood when they are dense. This 
process continues until the density-connected cluster is 
completely found. Then, a new unvisited point found out and 
processed which leads to the discovery of a further cluster or 
noise. 
V.EXPERIMENTAL SETUP 
In our work for the comparison of various clustering 
algorithms we used Weka tool. Weka is one of data-mining 
tool which contains a collection of machine learning 
algorithms. Weka contains tools for pre-processing, 
classification, regression, clustering, association rules, and 
visualization of data. In our work we made a dataset of egit 
software form the pfCDA software and svnsearch.org site. 
Dataset consists of three attributes class, depends and change. 
Classes with similar characteristics are grouped. We created 
database using Excel work-sheet in a .CSV file format. For 
our work we made an .arff file format from the .CSV file 
format. In our work we compared four clustering algorithms 
(K-mean, Hierarchal, EM, Density based) on the basis of 
Number of cluster, Cluster instances, Square error, Time taken 
to build model and Log likelihood. We showed training set, 
classes to cluster evaluation and visualization of cluster in our 
work. We used these algorithms one by one in weka tool and 
found their results and made a comparison table. 
V1.RESULTS ANALYSIS 
From Weka tool we found results using all algorithms that are 
shown in table4.1. This comparison table shows that for 
similar clustered data these algorithms give different results. 
Form this comparison table we find that k-mean algorithm 
provides better results than hierarchical and EM algorithm. It 
has better time for building a model than hierarchical and EM 
but it takes more time than Density based algorithms. We also 
find that log likelihood value of density based algorithm is 
higher. Form result we find that k-mean is a faster and safer 
algorithm than other algorithms we used. 
All Rights Reserved © 2014 IJARTES Visit: www.ijartes.org Page 21
International Journal of Advanced Research in Technology, Engineering and Science (A Bimonthly Open Access Online 
Journal) Volume1, Issue2, Sept-Oct, 2014.ISSN:2349-7173(Online) 
Name of 
algorithm 
Numbe 
r of 
cluster 
Cluster 
instance 
s 
Squar 
e 
error 
Time 
taken 
to 
build 
model 
Log 
likelihoo 
d 
k-mean 4 30% 602 0.03 
secon 
d 
28% 
22% 
20% 
Hierarch 
al 
4 52% 0.19 
secon 
d 
1% 
27% 
20% 
EM 4 30% 2.68 
secon 
d 
-11.9224 
20% 
22% 
28% 
Density 
based 
4 30% 0.02 
secon 
d 
-11.8997 
28% 
22% 
20% 
Table 4.1: Result of comparison of four clustering algorithms 
VII.CONCLUSION 
k-mean, EM, density based clustering algorithm have same 
clustered instances, but EM algorithm take more time to build 
cluster that’s why k-mean and density based algorithm are 
better than EM algorithm. Density based algorithm take less 
time to build a cluster but it does not better than the k-mean 
algorithm because density based algorithm has high log 
likelihood value, if the value of log likelihood is high than it 
doesn’t make good cluster. Hence k-mean is best algorithm 
because it takes very less time to build a model. Hierarchal 
algorithm take more time than k-mean algorithm and cluster 
instances are also not good in hierarchal algorithm. 
REFERENCES 
[1] A Hinneburg and D. Keim, "An Efficient Approach to Clustering in 
Large Multimedia Databases with Noise”, Proceedings of KDD-98 
(1998). 
[2] Aastha Joshi and Rajneet Kaur “Comparative Study of Various 
Clustering Techniques in Data Mining” (2013). 
[3] Bharat Chaudhari, Manan Parikh “A Comparative Study of clustering 
algorithms Using weka tools” (2012) 
[4] Bhoj Raj Sharmaa and Aman Paula “Clustering Algorithms: Study 
and Performance Evaluation Using Weka Tool” (2013). 
[5] Charalampos Mavroforaki “Data mining with WEKA”. 
[6] Clifton and Christopher, “Encyclopaedia Britannica: Definition of 
data mining”, Retrieved 2010-12-09, 2010. 
[7] David Scuse and Peter Reutemann”WEKA Experimenter Tutorial for 
Version 3-5-5” 
[8] Daljit Kaur, Kiran Jyoti “Enhancement in the Performance of K-means 
Algorithm” (2013) 
[9] Ester M., Kriegel HP., Sander J and Xu X,“A density-based 
algorithm for discovering clusters in largespatial databases with 
noise”,Second International Conference on Knowledge Discovery 
and Data Mining, 1996. 
[10] Fayyad, Usama, Gregory Piatetsky, Shapiro and Padhraic Smyth 
"From Data Mining to Knowledge Discovery in Databases", 
Retrieved 2008-12-17, 1996. 
[11] Gengxin Chen, Saied A. Jaradat, Nila Banerjee “EVALUATION 
AND COMPARISON OF CLUSTERING ALGORITHMS IN 
ANGLYZING ES CELL GENE EXPRESSION DATA” (2002) 
[12] M. Ankerst, M. Breunig, H.P. Kriegel and J. Sander, “OPTICS: 
Ordering Points To Identify the Clustering Structure”, Proceedings of 
ACM SIGMOD ‘99, International Conference on Management of 
Data, Philadelphia, pp. 49-60, 1999. 
[13] Michael Steinbach George Karypis Vipin Kumar “A Comparison of 
Document Clustering Techniques” 
[14] Narendra Sharma, Aman Bajpai, Mr. Ratnesh Litoriya “Comparison 
the various clustering algorithms of weka tools” (2012). 
[15] Pallavi, Sunila Godara “A Comparative Performance Analysis of 
Clustering Algorithms”. 
[16] Prajwala T R1, Sangeeta V I “Comparative Analysis of EM 
Clustering Algorithm and Density Based Clustering Algorithm Using 
WEKA tool.” (2014). 
[17] Sonam Narwal and Mr. Kamaldeep Mintwal “Comparison the 
Various Clustering and Classification Algorithms of WEKA Tools” 
(2013) 
[18] T.Balasubramanian, R.Umarani “Clustering as a Data Mining 
Technique in Health Hazards of High levels of Fluoride in Potable 
Water” (2012). 
[19] Vishal Shrivastava, Prem narayan Arya “A Study of Various 
Clustering Algorithms on Retail Sales Data” (2012) 
All Rights Reserved © 2014 IJARTES Visit: www.ijartes.org Page 22

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Ijartes v1-i2-006

  • 1. International Journal of Advanced Research in Technology, Engineering and Science (A Bimonthly Open Access Online Journal) Volume1, Issue2, Sept-Oct, 2014.ISSN:2349-7173(Online) Comparison of Different Clustering Algorithms using WEKA Tool Priya Kakkar1, Anshu Parashar2 ______________________________________________ Abstract: Data Mining is a process of extracting useful information from a large dataset and Clustering is one of important technique in data mining process, whose main purpose is to group data of similar types into clusters and finding a structure among unlabelled data. In this paper we have taken four different clustering algorithm i.e. K-Mean algorithm, Hierarchical algorithm, Density based algorithm, EM algorithm. All these algorithms are applied on data of egit software repositories and depends or dependent classes. In this paper to compare and analyze these four algorithms with respect to time to build a model, cluster instances, squared errors and log likelihood by using Weka tool. _________________________________________________ Keywords: Data Mining, Clustering, K-mean, Weka tool, DBSCAN __________________________________________________ I.INTRODUCTION Data mining is a field used to find out the data hidden in your clusters of data or massive set of data. Data mining is an important tool to convert the data into information. It is used in a different field of practices, such as marketing, fraud detection and scientific discovery. Data mining is the also used for extracting patterns from data. It can be used to uncover patterns in data but is often carried out only on sample of data. The mining process will be ineffective if the samples are not good representation of the larger body of the data. The discovery of a particular pattern in a particular set of data does not necessarily mean that pattern is found elsewhere in the larger data from which that sample was drawn. An important part of the method is the verification and validation of patterns on other samples of data. A primary reason for using data mining is to assist in the analysis of collection of observations of behavior. Data mining is the analysis step of the "Knowledge Discovery in Databases" process and is the process that attempts to discover patterns from large data sets. The main aim of the data mining process is to extract information from a data set and transform it into an understandable format for further use. ________________________________________________ First Author’s Name: Priya Kakkar, Department of Computer Science & Engineering, HCTM Technical Campus, Kaithal, India. Second Author’s Name: Anshu Parashar, Department of Computer Science & Engineering, HCTM Technical Campus, Kaithal, India. __________________________________________________________ Clustering is the task of assigning a set of objects into groups (called clusters) so that the objects in the same cluster are more similar to each other than to those in other clusters. A cluster is therefore a collection of objects which are “similar” between them and are “dissimilar” to the objects belonging to other clusters. Clustering is a common technique used for statistical data analysis in many fields like machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. II.CLUSTERING METHODS The goal of clustering is to organize objects which are related to each other or have similar characteristics. Clustering groups similar objects (item) into same group. We use different methods for clustering. · Partitioning clustering The partitioning method uses a set of M clusters and each object belongs to one cluster. Each cluster can be represented by a centroid or a cluster representative; that is a description of all the objects contained in a cluster. This description will depend on the type of the object which is clustered. In real-valued data the arithmetic mean of the attribute vectors for all objects within a cluster provides an appropriate representative while alternative types of centroid may be required in other cases. If the number of the clusters is large then centroid can be further clustered which produces hierarchy within a dataset. · Hierarchical clustering Flat clustering is efficient and conceptually simple but it has a number of drawbacks. The algorithms require a pre-specified number of clusters as input and are nondeterministic. Hierarchical clustering outputs a hierarchical structure that is more informative than the unstructured set of clusters formed by flat clustering. Hierarchical clustering also does not need to specify the number of clusters in advance. In hierarchical clustering clusters are created either by top-down or bottom-up fashion by recursive partitioning. Hierarchical clustering are of two types: - Hierarchical Agglomerative methods, Hierarchical Divisive clustering. · Density based clustering Density-based clustering algorithms try to find clusters based on density of data points in a region. The key idea behind density-based clustering is that for each instance of a cluster the neighborhood of a given radius (Eps) has to contain at least a minimum number of instances (MinPts). Density based clustering is based on probability distribution and points from All Rights Reserved © 2014 IJARTES Visit: www.ijartes.org Page 20
  • 2. International Journal of Advanced Research in Technology, Engineering and Science (A Bimonthly Open Access Online Journal) Volume1, Issue2, Sept-Oct, 2014.ISSN:2349-7173(Online) one distribution are assumed to be part of one cluster. This method identifies the clusters and their parameters. IV.VARIOUS CLUSTARING ALGORITHMS · k-mean clustering K-means is a widely used partition based clustering method because it can be easily implemented and most efficient one in terms of the execution time. k-mean clustering group items into k groups. This grouping is done on the basis of minimizing the sum of squared distances between items and the corresponding centroid. A centroid is "center of mass of a geometric object of uniform density". K-Means Algorithm: In k-mean algorithm each cluster’s center is represented by mean value of objects in the cluster. Input: k: the number of clusters. D: data set containing n objects. Output: A set of k clusters. Method: 1. Arbitrarily choose k objects from D as the initial cluster centers. 2. Repeat. 3. Reassign each object to the cluster to which the object is most similar based on the mean value of the objects in the cluster. 4. Update the cluster means. 5. until no change. · EM algorithm In cases where the equations cannot be solved directly we use a special algorithm known as The EM algorithm. EM stands for Expectation and Maximization which is part of data mining tools.The EM algorithm is used to find most likelihood parameters in a model. These models Contains latent variable and use likelihood functions in addition to unknown parameters and known data observations. It contains either missing value among the data, or the model can be simplified by assuming the existence of additional unobserved data points. To find out solutions it requires taking derivatives of likelihood functions with respect to all unknown values. The result is typically a set of interlocking equations in which the solution to the parameters requires the values of the latent variables and vice-versa, but substituting one set of equations into the other produces an unsolvable equation. EM algorithm pick arbitrarily values for one of sets and use these values to estimate the second set then use these values to estimate first set and this will continue until the resulting values converge to fixed points. · Density-based spatial clustering of applications with noise (DBSCAN) Algorithm Density based spatial clustering of application with noise is one of Density based algorithm. It separates data points into three parts: Core points (points that are at the interior of a cluster), Border points (points which fall within neighborhood of core point) and Noise points (point that is not a core point or a border point).DBSCAN starts with an arbitrary instance (p) in data set (D) and finds all values of D within Eps and MinPts. The algorithm uses a spatial data structure to place points within Eps from the core points of the clusters. It starts with an arbitrary starting point that has not been visited and point’s Eps-neighborhood is found out and if it contains sufficiently many points, a cluster is started. Otherwise, point is recognized as noise. This point might later be found within Eps-environment of a different point and hence it’s to made part of a cluster. If a point is found a dense part of a cluster then its Eps-neighborhood is also part of that cluster. Hence, all points which are found within the Eps-neighborhood are also added like their own Eps-neighborhood when they are dense. This process continues until the density-connected cluster is completely found. Then, a new unvisited point found out and processed which leads to the discovery of a further cluster or noise. V.EXPERIMENTAL SETUP In our work for the comparison of various clustering algorithms we used Weka tool. Weka is one of data-mining tool which contains a collection of machine learning algorithms. Weka contains tools for pre-processing, classification, regression, clustering, association rules, and visualization of data. In our work we made a dataset of egit software form the pfCDA software and svnsearch.org site. Dataset consists of three attributes class, depends and change. Classes with similar characteristics are grouped. We created database using Excel work-sheet in a .CSV file format. For our work we made an .arff file format from the .CSV file format. In our work we compared four clustering algorithms (K-mean, Hierarchal, EM, Density based) on the basis of Number of cluster, Cluster instances, Square error, Time taken to build model and Log likelihood. We showed training set, classes to cluster evaluation and visualization of cluster in our work. We used these algorithms one by one in weka tool and found their results and made a comparison table. V1.RESULTS ANALYSIS From Weka tool we found results using all algorithms that are shown in table4.1. This comparison table shows that for similar clustered data these algorithms give different results. Form this comparison table we find that k-mean algorithm provides better results than hierarchical and EM algorithm. It has better time for building a model than hierarchical and EM but it takes more time than Density based algorithms. We also find that log likelihood value of density based algorithm is higher. Form result we find that k-mean is a faster and safer algorithm than other algorithms we used. All Rights Reserved © 2014 IJARTES Visit: www.ijartes.org Page 21
  • 3. International Journal of Advanced Research in Technology, Engineering and Science (A Bimonthly Open Access Online Journal) Volume1, Issue2, Sept-Oct, 2014.ISSN:2349-7173(Online) Name of algorithm Numbe r of cluster Cluster instance s Squar e error Time taken to build model Log likelihoo d k-mean 4 30% 602 0.03 secon d 28% 22% 20% Hierarch al 4 52% 0.19 secon d 1% 27% 20% EM 4 30% 2.68 secon d -11.9224 20% 22% 28% Density based 4 30% 0.02 secon d -11.8997 28% 22% 20% Table 4.1: Result of comparison of four clustering algorithms VII.CONCLUSION k-mean, EM, density based clustering algorithm have same clustered instances, but EM algorithm take more time to build cluster that’s why k-mean and density based algorithm are better than EM algorithm. Density based algorithm take less time to build a cluster but it does not better than the k-mean algorithm because density based algorithm has high log likelihood value, if the value of log likelihood is high than it doesn’t make good cluster. Hence k-mean is best algorithm because it takes very less time to build a model. Hierarchal algorithm take more time than k-mean algorithm and cluster instances are also not good in hierarchal algorithm. REFERENCES [1] A Hinneburg and D. Keim, "An Efficient Approach to Clustering in Large Multimedia Databases with Noise”, Proceedings of KDD-98 (1998). [2] Aastha Joshi and Rajneet Kaur “Comparative Study of Various Clustering Techniques in Data Mining” (2013). [3] Bharat Chaudhari, Manan Parikh “A Comparative Study of clustering algorithms Using weka tools” (2012) [4] Bhoj Raj Sharmaa and Aman Paula “Clustering Algorithms: Study and Performance Evaluation Using Weka Tool” (2013). [5] Charalampos Mavroforaki “Data mining with WEKA”. [6] Clifton and Christopher, “Encyclopaedia Britannica: Definition of data mining”, Retrieved 2010-12-09, 2010. [7] David Scuse and Peter Reutemann”WEKA Experimenter Tutorial for Version 3-5-5” [8] Daljit Kaur, Kiran Jyoti “Enhancement in the Performance of K-means Algorithm” (2013) [9] Ester M., Kriegel HP., Sander J and Xu X,“A density-based algorithm for discovering clusters in largespatial databases with noise”,Second International Conference on Knowledge Discovery and Data Mining, 1996. [10] Fayyad, Usama, Gregory Piatetsky, Shapiro and Padhraic Smyth "From Data Mining to Knowledge Discovery in Databases", Retrieved 2008-12-17, 1996. [11] Gengxin Chen, Saied A. Jaradat, Nila Banerjee “EVALUATION AND COMPARISON OF CLUSTERING ALGORITHMS IN ANGLYZING ES CELL GENE EXPRESSION DATA” (2002) [12] M. Ankerst, M. Breunig, H.P. Kriegel and J. Sander, “OPTICS: Ordering Points To Identify the Clustering Structure”, Proceedings of ACM SIGMOD ‘99, International Conference on Management of Data, Philadelphia, pp. 49-60, 1999. [13] Michael Steinbach George Karypis Vipin Kumar “A Comparison of Document Clustering Techniques” [14] Narendra Sharma, Aman Bajpai, Mr. Ratnesh Litoriya “Comparison the various clustering algorithms of weka tools” (2012). [15] Pallavi, Sunila Godara “A Comparative Performance Analysis of Clustering Algorithms”. [16] Prajwala T R1, Sangeeta V I “Comparative Analysis of EM Clustering Algorithm and Density Based Clustering Algorithm Using WEKA tool.” (2014). [17] Sonam Narwal and Mr. Kamaldeep Mintwal “Comparison the Various Clustering and Classification Algorithms of WEKA Tools” (2013) [18] T.Balasubramanian, R.Umarani “Clustering as a Data Mining Technique in Health Hazards of High levels of Fluoride in Potable Water” (2012). [19] Vishal Shrivastava, Prem narayan Arya “A Study of Various Clustering Algorithms on Retail Sales Data” (2012) All Rights Reserved © 2014 IJARTES Visit: www.ijartes.org Page 22