
Review Article


A Survey of Partition based Clustering Algorithms in Data Mining: An Experimental Approach


T. Velmurugan
and
T. Santhanam


ABSTRACT

Clustering is one of the most important research areas in
the field of data mining. Clustering means creating groups of objects based
on their features in such a way that the objects belonging to the same groups
are similar and those belonging in different groups are dissimilar. Clustering
is an unsupervised learning technique. Data clustering is the subject of active
research in several fields such as statistics, pattern recognition and machine
learning. From a practical perspective clustering plays an outstanding role
in data mining applications in many domains. The main advantage of clustering
is that interesting patterns and structures can be found directly from very
large data sets with little or none of the background knowledge. Clustering
algorithms can be applied in many areas, for instance marketing, biology, libraries,
insurance, cityplanning, earthquake studies and www document classification.
Data mining adds to clustering the complications of very large datasets with
very many attributes of different types. This imposes unique computational requirements
on relevant clustering algorithms. A variety of algorithms have recently emerged
that meet these requirements and were successfully applied to reallife data
mining problems. They are subject of this survey. Also, this survey explores
the behavior of some of the partition based clustering algorithms and their
basic approaches with experimental results. 




Received:
August 13, 2010; Accepted: October 28, 2010;
Published: December 01, 2010 

INTRODUCTION
The goal of this survey is to provide a comprehensive review of different partition
based clustering algorithms in data mining. Clustering is a division of data
into groups of similar objects. Each group, called cluster, consists of objects
that are similar between themselves and dissimilar to objects of other groups.
Representing data by fewer clusters necessarily loses certain fine details (akin
to lossy data compression), but achieves simplification. It represents many
data objects by few clusters and hence, it models data by its clusters. Data
modeling puts clustering in a historical perspective rooted in mathematics,
statistics and numerical analysis. From a machine learning perspective clusters
correspond to hidden patterns, the search for clusters is unsupervised learning
and the resulting system represents a data concept. Clustering is a method of
unsupervised learning and a common technique for statistical data analysis used
in many fields, including machine learning, data mining, pattern recognition,
image analysis and bioinformatics. Besides the term clustering, there are a
number of terms with similar meanings, including automatic classification, numerical
taxonomy, botryology and typological analysis. Therefore, clustering is unsupervised
learning of a hidden data concept (Berkhin, 2002; Dunham,
2003; Han and Kamber, 2006; Jain
et al., 1999).
Data mining deals with large databases that impose on cluster analysis. Some
of the challenges led to the emergence of powerful broadly applicable data mining
clustering methods surveyed below. A variety of clustering algorithms have been
used for research in the field of data mining (Berkhin, 2002;
Dunham, 2003; Han and Kamber, 2006;
Xiong et al., 2006; Park et
al., 2009; Khan and Ahmad, 2004; Alexander
and Caponnetto, 2007). They are organized into the following categories:
partitioning methods, hierarchical methods, densitybased methods, gridbased
methods, modelbased methods, methods for high dimensional data and constraintbased
clustering. Some of the above methods use the distance measure for finding the
clusters. The scope of this survey is modest: to provide an introduction to
cluster analysis in the field of data mining, where, it is to define data mining
to be the discovery of useful, but nonobvious, information or patterns in large
collections of data. Much of this survey is necessarily consumed by providing
a general background for cluster analysis, but also which discuss a number of
partitions based clustering techniques that have recently been developed specifically
for data mining. While, the survey strives to be selfcontained from a conceptual
point of view, many details have been omitted.
PARTITIONING METHODS
The term cluster does not have a precise definition. However, several working
definitions of a cluster are commonly used. A cluster is a set of points such
that any point in a cluster is closer (or more similar) to every other point
in the cluster than to any point not in the cluster. Sometimes a threshold is
used to specify that all the points in a cluster must be sufficiently close
(or similar) to one another (Jain and Dubes, 1988). However,
in many sets of data, a point on the edge of a cluster may be closer (or more
similar) to some objects in another cluster than to objects in its own cluster.
Consequently, many clustering algorithms use the centerbased cluster criterion.
The center of a cluster is often a centroid, the average of all the points in
the cluster, or a medoid, the most representative point of a cluster.
A partitioning method first creates an initial set of k partitions, where,
parameter k is the number of partitions to construct. It then uses an iterative
relocation technique that attempts to improve the partitioning by moving objects
from one group to another. These clustering techniques create a onelevel partitioning
of the data points. There are a number of such techniques, but this survey shall
only describe three approaches namely Kmeans, Kmedoids and fuzzy Cmeans.
Except these three techniques, to deal with large data sets, a samplingbased
method CLARA (Clustering LARge Applications) is used. To improve the quality
and scalability of CLARA, a Kmedoid type algorithm called CLARANS (Clustering
Large Applications based upon RANdomized Search) was proposed, which combines
the sampling technique with PAM (Partitioning Around Medoids) (Jain
et al., 1999; Han and Kamber, 2006). All these
techniques are based on the idea that a center point can represent a cluster.
For Kmeans, the notion of a centroid is used, which is the mean or median point
of a group of points. A centroid almost never corresponds to an actual data
point. For Kmedoid, the notion of a medoid is used, which is the most representative
(central) point of a group of points. Kmeans is a simple algorithm that has
been adapted to many problem domains. It can see that the Kmeans algorithm
is a good candidate for extension to work with fuzzy feature vectors (Berkhin,
2002; Dunham, 2003; Han and Kamber,
2006; Jain et al., 1999; Kaufman
and Rousseeuw, 1990). Therefore the algorithm with fuzzy feature is called
the Fuzzy CMeans (FCM) algorithm.
As stated, this survey discusses only the partition based algorithms in data
mining. Therefore, the algorithms Kmeans, Kmedoids and fuzzy Cmeans are examined
one by one to analyze based on the distance between the various input data points.
The clusters are formed according to the distance between data points and cluster
centers are formed for each cluster. The number of clusters (by giving the Kvalue)
is specified by the user. The data points in each cluster are displayed by different
colors (one color for one cluster) and the execution time for each cluster and
the total time is calculated in milliseconds (Velmurugan
and Santhanam, 2010a). This research work does not use any existing data
set that they were available anywhere including the internet. The data points
are created by own method by using a JAVA program for all the three types of
algorithms discussed here.
KMEANS ALGORITHM
KMeans is one of the simplest unsupervised learning algorithms that solve
the well known clustering problem. The procedure follows a simple and easy way
to classify a given data set through a certain number of clusters (assume k
clusters) fixed a priori (Borah and Ghose, 2009; Alexander
and Caponnetto, 2007). The main idea is to define k centroids, one for each
cluster. These centroids should be placed in a cunning way because of different
location causes different result. So, the better choice is to place them as
much as possible far away from each other. The next step is to take each point
belonging to a given data set and associate it to the nearest centroid. When,
no point is pending, the first step is completed and an early group age is done.
At this point it is necessary to recalculate k new centroids as bar centers
of the clusters resulting from the previous step. After obtaining these k new
centroids, a new binding has to be done between the same data set points and
the nearest new centroid. A loop has been generated. As a result of this loop,
one may notice that the k centroids change their location step by step until
no more changes are done (Xiong et al., 2006;
Kanungo et al., 2003; Bradley
and Fayyad, 1998). In other words centroids do not move any more. Finally,
this algorithm aims at minimizing an objective function, in this case a squared
error function. The objective function:
where, x_{i}^{(j)}c_{j}^{2}
is a chosen distance measure between a data point x_{i}^{(j)}
and the cluster centre c_{j}, is an indicator of the distance of the
n data points from their respective cluster centers. The algorithm is composed
of the following steps:
Step 1: 
Place K points into the space represented by the objects
that are being clustered. These points represent initial group centroids 
Step 2: 
Assign each object to the group that has the closest centroid 
Step 3: 
When, all objects have been assigned, recalculate the positions of
the K centroids 
Step 4: 
Repeat steps 2 and 3 until the centroids no longer move 
This produces a separation of the objects into groups from which the metric
to be minimized can be calculated. Always the algorithm can be proved that the
procedure will terminate, the Kmeans algorithm does not necessarily find the
most optimal configuration, corresponding to the global objective function minimum.
The algorithm is also significantly sensitive to the initial randomly selected
cluster centers. The Kmeans algorithm can be run multiple times to reduce this
effect (Dunham, 2003; Han and Kamber,
2006; Dhillon et al., 2005). Kmeans is a
simple algorithm that has been adapted to many problem domains and it is a good
candidate to work for a randomly generated data points. One of the most popular
heuristics for solving the Kmeans problem is based on a simple iterative scheme
for finding a locally minimal solution (Borah and Ghose,
2009; Khan and Ahmad, 2004; Alsabti
et al., 1998). This algorithm is often called the Kmeans algorithm.
There are some difficulties in using Kmeans for clustering data. This is proved
by several times in this current as well as in the past research and an oftrecurring
problem has to do with the initialization of the algorithm. Kmeans is a simple
algorithm that has been adapted to many problem domains.
The experimental results for Kmeans clustering algorithm is shown in Fig.
1. The data points are created manually in the applet window by pressing
the mouse buttons. Actually, three types of data points can be created in the
applet window (Velmurugan and Santhanam, 2008). They
are normal distribution, uniform distributions and the third one is manual creation
of data points. This survey uses only the manual creation of data points. The
other methods of creation of data points are used in other study (Velmurugan
and Santhanam, 2010b). The resulting clusters of the distribution of data
points for Kmeans algorithm is presented in Fig. 1. The number
of clusters and the data points is given by the user during the execution of
the program. The number of data points is 1000 and the number of clusters given
by the user is 5 (k = 5). The algorithm is repeated 1000 times (one iteration
for each data point) to get efficient output. The cluster centers (centroids)
are calculated for each cluster by its mean value and clusters are formed depending
upon the distance between data points. For different input data points, the
algorithm gives different types of outputs. The input data points are generated
in red color and the output of the algorithm is displayed in different colors
as shown in Fig. 1. The center point of each cluster is displayed
in white color. The execution time of each run is calculated in milliseconds.
The time taken for execution of the algorithm varies from one run to another
run and also it differs from one computer to another computer. The number of
data points is the size of the cluster. If the number of data points are 1000
then the algorithm is repeated the same one thousand times. For each data point,
the algorithm executes one time. From the results it is clear that the algorithm
takes 3859 m sec for 1000 data points and 5 clusters. The execution time for
each cluster is also calculated. The sum of the execution time of all clusters
is 3829 m sec. The difference is 30 m sec. This time is the execution time for
other codes in the program.
KMEDOIDS ALGORITHM
The Kmeans algorithm is sensitive to outliers since an object with an extremely
large value may substantially distort the distribution of data. How might the
algorithm be modified to diminish such sensitivity? Instead of taking the mean
value of the objects in a cluster as a reference point, a medoid can be used,
which is the most centrally located object in a cluster. Thus, the partitioning
method can still be performed based on the principle of minimizing the sum of
the dissimilarities between each object and its corresponding reference point.
This forms the basis of the Kmedoids method (Han and Kamber,
2006; Jain et al., 1999; Kaufman
and Rousseeuw, 1990). The basic strategy of Kmedoids clustering algorithms
is to find k clusters in n objects by first arbitrarily finding a representative
object (the medoids) for each cluster. Each remaining object is clustered with
the medoid to which it is the most similar. Kmedoids method uses representative
objects as reference points instead of taking the mean value of the objects
in each cluster. The algorithm takes the input parameter k, the number of clusters
to be partitioned among a set of n objects (Park et al.,
2009; Dunham, 2003; Han and Kamber,
2006; Zeidat and Eick, 2004; Sheng
and Liu, 2006).
A typical KMediods algorithm for partitioning based on medoid or central objects
is as follows:
Like this algorithm, a Partitioning Around Medoids (PAM) was one of the first
Kmedoids algorithms introduced. It attempts to determine k partitions for n
objects. After an initial random selection of k medoids, the algorithm repeatedly
tries to make a better choice of medoids. Therefore, the algorithm is often
called as representative object based algorithm. Figure 2
is the output for one of the executions of Kmedoids algorithm for the manual
creation of input data points.
Here, also the number of data points is 1000 and the number clusters chosen
by the user is 5. By comparing the results of both the algorithms Kmeans and
Kmedoids (Velmurugan and Santhanam, 2010a), it can
be easily understood that there is much difference in the execution time. The
Kmeans algorithm takes 3859 m sec whereas the Kmedoids algorithm takes 4109
m sec (Velmurugan and Santhanam, 2009a).
FUZZY CMEANS ALGORITHM Traditional clustering approaches generate partitions; in a partition, each pattern belongs to one and only one cluster. Hence, the clusters in a hard clustering are disjoint. Fuzzy clustering extends this notion to associate each pattern with every cluster using a membership function. The output of such algorithms is a clustering, but not a partition. Fuzzy clustering is a widely applied method for obtaining fuzzy models from data. It has been applied successfully in various fields including geographical surveying, finance or marketing. This method is frequently used in pattern recognition. It is based on minimization of the following objective function:
where, m is any real number greater than 1, u_{ij} is the degree of
membership of x_{i} in the cluster j, x_{i} is the ith of ddimensional
measured data, c_{j} is the ddimension center of the cluster and *
is any norm expressing the similarity between any measured data and the center
(AlZoubi et al., 2007; Yong
et al., 2004). Fuzzy partitioning is carried out through an iterative
optimization of the objective function shown above, with the update of membership
u_{ij} and the cluster centers c_{j} by:
This iteration will stop when max_{ij} {u_{ij}^{(k+1)}u_{ij}^{(k)}}<ε,
where, ε is a termination criterion between 0 and 1, whereas k are the
iteration steps. This procedure converges to a local minimum or a saddle point
of J_{m}. The algorithm is composed of the following steps:
Step 1: 
Initialize U = [u_{ij}] matrix, U^{(0)} 
Step 2: 
At kstep: calculate the centers vectors C^{(k)} = [c_{j}]
with U^{(k)} 
Step 3: 
Update U^{(k)}, U^{(k+1)} 
Step 4: 
If  U^{(k+1)}U^{(k)}<ε then STOP; otherwise
return to step 2 
In this algorithm, data are bound to each cluster by means of a membership function, which represents the fuzzy behavior of the algorithm. To do that, the algorithm has to build an appropriate matrix named U whose factors are numbers between 0 and 1 and represent the degree of membership between data and centers of clusters. FCM clustering techniques are based on fuzzy behavior and provide a natural technique for producing a clustering where membership weights have a natural (but not probabilistic) interpretation. This algorithm is similar in structure to the Kmeans algorithm and also behaves in a similar way.
However, when the FCM algorithm is run on the given 1000 data points with C
= 5, five clusters are identified as shown in Fig. 3. The
result of the algorithm is displayed in the same figure. Here, also the number
of data points is 1000 and the number of clusters chosen by the user is 5. This
algorithm takes 4000 m sec to get the output. This is much better than the Kmedoids
algorithm, but not superior to Kmeans algorithm. The data points to all the
three algorithms are created manually in this research in applet window, not
by using any formula like boxmuller formula. The normal and uniform distribution
of data points are created by using the boxmuller formula (Velmurugan
and Santhanam, 2009b). They are not discussed in this study. Table
1 gives the comparative result of all these three algorithms. For all the
three algorithms, the program is executed many times and the results are analyzed
based on the number of data points and the number of clusters. The behavior
of the algorithms is analyzed based on observations. The performance of the
algorithms have also been analyzed for several executions by considering different
data points (for which the results are not shown) as input (500, 1000 and 2000
data points etc.) and the number of clusters are from 5 to 10 (for which also
the results are not shown), the outcomes are found to be highly satisfactory
(Velmurugan and Santhanam, 2009b).
Table 1:  Results
of algorithms 

 Fig. 3:  Fuzzy
Cmeans output 
From Table 1, it is easy to understand that Kmeans algorithm
takes very less time and Kmedoids algorithm takes more time than the Kmeans
for clustering the data points, but the FCM algorithm act as intermediary between
the previous two algorithms.
Cluster analysis is still an active field of development. Many cluster analysis
techniques do not have a strong formal basis. Cluster analysis is a rather adhoc
field (Berkhin, 2002; Dunham, 2003;
Han and Kamber, 2006; Xiong et al.,
2006; Park et al., 2009). There are a wide
variety of clustering techniques. Comparisons among different clustering techniques
are difficult. All techniques seem to impose a certain structure on the data
and yet few authors describe the type of limitations being imposed. In spite
of all these problems, clustering analysis is a useful (and interesting) field.
In summary, clustering is an interesting, useful and challenging problem. It
has great potential in applications like object recognition, image segmentation
and information filtering and retrieval. However, it is possible to exploit
this potential only after making several designs choices carefully. The advantage
of the partitionbased algorithms that they use an iterative way to create the
clusters, but the drawback is that the number of clusters has to be determined
in advance and only spherical shapes can be determined as clusters (Davies
and Bouldin, 1979).
CONCLUSION Usually the time complexity varies from one processor to another processor, which depends on the speed and the type of the system. The partition based algorithms work well for finding sphericalshaped clusters in small to mediumsized data points. The advantage of the Kmeans algorithm is its favorable execution time. Its drawback is that the user has to know in advance how many clusters are searched for. From the experimental results, by several executions of the program for the proposed three algorithms, the following results were obtained. It is observed that Kmeans algorithm is efficient for smaller data sets and Kmedoids algorithm seems to perform better for large data sets. The performance of FCM is intermediary between them. FCM produces close results to Kmeans clustering, yet it requires more computation time than Kmeans because of the fuzzy measures calculations involved in the algorithm. From Table 1, it is notorious that for 1000 manually created data points with five clusters, Kmeans algorithm is very consistent when compared with the other two algorithms. Further, it stamps its superiority in terms of its lesser execution time.

REFERENCES 
AlZoubi, M.B., A. Hudaib and B. AlShboul, 2007. A fast fuzzy clustering algorithm. Proceedings of the 6th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases, February 2007, Corfu Island, Greece, pp: 2832.
Alexander, R. and A. Caponnetto, 2007. Stability of KMeans Clustering, Advances in Neural Information Processing Systems 12. MIT Press, Cambridge, MA, pp: 216222.
Alsabti, K., S. Ranka and V. Singh, 1998. An efficient kmeans clustering algorithm. Proc. First Workshop High Performance Data Mining. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.110.903&rep=rep1&type=pdf.
Berkhin, P., 2002. Survey of Clustering Data Mining Techniques. Accure Software Inc., San Jose, CA., USA.
Borah, S. and M.K. Ghose, 2009. Performance analysis of AIMKMeans and KMeans in quality cluster generation. J. Comput., 1: 175178. Direct Link 
Bradley, P.S. and U.M. Fayyad, 1998. Refining initial points for Kmeans clustering. Proceedings of the 15th International Conference on Machine Learning, July 2427, 1998, Morgan Kaufmann, San Francisco, pp: 9199.
Davies, D.L. and D.W. Bouldin, 1979. A cluster separation measure. IEEE. Trans. Pattern Anal. Mach. Intel., 1: 224227. CrossRef 
Dhillon, I., Y. Guan and B. Kulis, 2005. A unified view of kernel kmeans, spectral clustering and graph partitioning. Technical Report TR0425, UTCS Technical Report.
Dunham, M., 2003. Data Mining: Introductory and Advanced Topics. Prentice Hall, USA.
Han, J. and M. Kamber, 2006. Data Mining: Concepts and Techniques. 2nd Edn., Morgan Kaufmann Publisher, San Fransisco, USA., ISBN: 1558609016.
Jain, A.K. and R.C. Dubes, 1988. Algorithms for Clustering Data. Prentice Hall Inc., Englewood Cliffs, USA., ISBN: 013022278X, Pages: 320.
Jain, A.K., M.N. Murty and P.J. Flynn, 1999. Data clustering: A review. ACM Comput. Surveys, 31: 264323. CrossRef  Direct Link 
Kanungo, T., D.M. Mount, N.S. Netanyahu, C.D. Piatko, R. Silverman and A.Y. Wu, 2003. A local search approximation algorithm for kmeans clustering. July 14, 2003. http://www.cs.umd.edu/~mount/Projects/KMeans/kmlocalcgta.pdf.
Kaufman, L. and P.J. Rousseeuw, 1990. Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley and Sons, New York, ISBN: 0471878766, pp: 342.
Khan, S.S. and A. Ahmad, 2004. Cluster center initialization algorithm for KMeans clustering. Pattern Recognition Lett., 25: 12931302. CrossRef 
Park, H.S., J.S. Lee and C.H. Jun, 2009. A KMeansLike Algorithm for KMedoids Clustering and Its Performance. Department of Industrial and Management Engineering, POSTECH, South Korea.
Sheng, W. and X. Liu, 2006. A genetic kmedoids clustering algorithm. J. Heuristics, 12: 447466. CrossRef 
Velmurugan, T. and T. Santhanam, 2008. Performance analysis of kmeans and kmedoids clustering algorithms for a randomly generated data set. Proceedings of the International Conference on Systemics, Cybernetics and Informatics, Jan. 08, Hyderabad, India, pp: 578583.
Velmurugan, T. and T. Santhanam, 2009. Clustering of random data points using KMeans and fuzzyc means clustering algorithms. Proceedings of the IEEE International Conference on Emerging Trends in Computing, Jan. 09, Virudhunagar, India, pp: 177180.
Velmurugan, T. and T. Santhanam, 2009. A practical approach of kmedoids clustering algorithm for artificial data points. Proceedings of the International Conference on Semantics, Ebusiness and ECommerce, Nov. 09, Trichirappalli, India, pp: 4550.
Velmurugan, T. and T. Santhanam, 2010. Performance evaluation of kmeans and fuzzy cmeans clustering algorithms for statistical distributions of input data points. Eur. J. Sci. Res., Vol. 46, No. 3
Velmurugan, T. and T. Santhanam, 2010. Computational complexity between Kmeans and Kmedoids clustering algorithms for normal and uniform distributions of data points. J. Comput. Sci., 6: 363368. CrossRef  Direct Link 
Xiong, H., J. Wu and J. Chen, 2006. Kmeans clustering versus validation measures: A data distribution perspective. Proceedings of the 12th ACM SIGKDD International Conference Knowledge Discovery and Data Mining, (KDDM'06), USA., pp: 779878.
Yong, Y., Z. Chongxun and L. Pan, 2004. A novel fuzzy cmeans clustering algorithm for image thresholding. Measurement Sci. Rev., 4: 99. Direct Link 
Zeidat, N. and C.F. Eick, 2004. Kmedoidstyle clustering algorithms for supervised summary generation. Proceedings of the International Conference on Machine Learning. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.128.9219&rep=rep1&type=pdf.



