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International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 02 | Feb -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1213
Review of Existing Methods in K-means Clustering Algorithm
MS. Kavita Shiudkar1, Prof. Sachine Takmare2
1 ME CSE, Bharti Vidyapeeth college of Engineering, Kolhapur, Maharashtra, India
2 Assistant Professor, Dept. of CSE, Bharti Vidyapeeth college of Engineering Kolhapur, Maharashtra, India
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Abstract – Data mining is the process of extracting
useful information from the large amount of data and
converting it into understandable form for further use.
Clustering is the process of grouping object attributes
and features such that the data objects in one group are
more similar than data objects inanothergroup.Butitis
now very challenging due to the sharply increase in the
large volume of data generated by number of
applications. Kmeans is a simple and widely used
algorithm for clustering data. But, the traditional k-
means is computationallyexpensive;sensitivetooutlier’s
i.e. unnecessary data and producesunstableresulthence
it becomes inefficient when dealing with very large
datasets. Solving these Issues is the subject of many
recent research works. In this paper, wewill doareviewon
k-means clustering algorithms.
Key Words: Initial Centroids, Clustering, Data mining, Data
sets, K-means clustering, Map-Reduce.
1. INTRODUCTION
Big Data is evolving term that describes any voluminous
amount of structured, semi-structured and unstructureddata.
It is characterized by “5Vs”, volume (size of data set), variety
(range of data type and source), velocity (speed of data in and
out), value (how useful the data is), and veracity (quality of
data). It creates challenges in their collection, processing,
management and analysis. As new data and updates are
constantly arriving, there is need of data mining to tackle
challenges.
The purpose of the data mining technique is to mine
information from a bulky data set and make over it into a
reasonable form for supplementary purpose. Data mining is
also known as the knowledge discovery in databases (KDD).
Technically, data mining is the process of finding patterns
among number of fields in large relational database. It is the
best process to differentiate between data and information.
Data mining consists of extract, transform, and load
transaction data onto the data warehouse system, Store and
manage the data in a multidimensional database system,
Provide data access to business analysts and information
technology professionals, analyze the data by application
software, Present the data in a useful format, such as a graph
or table.
2. CLUSTERING
It makes an important role in data analysis and data mining
applications. Data divides into similarobjectgroupsbasedon
their features, each data group will consist of collection of
similar objects in clusters. Clustering is a process of
unsupervised learning. Highly superior clusters have high
intra-class similarity and low inter-class similarity. Several
algorithms have been designed to perform clustering, each
one uses different principle. They are divided into
hierarchical, partitioning, density-based, model based
algorithms and grid-based.
Fig: 1 Clustering stages
There are two types of Clustering Partitioning and
Hierarchical Clustering.
1. Hierarchical Clustering- A set of nested clusters
organized in the form of tree.
2. Partitioning Clustering - A division of data
objects intosubsets(clusters)suchthateachdataobject
is in exactly one subset.
3. K-MEANS CLUSTERING
K-means clustering technique is widely used clustering
algorithm, which is most popular clustering algorithm that is
used in scientific and industrial applications.Itisamethodof
cluster analysis which is used to partition N objects into k
clusters in such a way that each object belongs to the cluster
Raw Input
Data
Data
Clusters
Clusters
Clustering
Algorithms
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 02 | Feb -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1214
with the nearest mean [3].
The Traditional KMeans algorithm is very simple [3]:
1. Select the value of K i.e. Initial centroids.
2. Repeat step 3 and 4 for all data points in dataset.
3. Find the nearest point from that centroids in the
Dataset.
4. Form K cluster by assigning each point to its closest
centroid.
5. Calculate the new global centroid for each cluster.
Properties of k-means algorithm [3]:
1. Efficient while processing large data set.
2. It works only on numeric values.
3. The shapes of clusters are convex.
K-means is the most commonly used partitioning
algorithm in cluster analysis because of its simplicity and
performance. But it has some restrictions when dealing with
very large datasets because of high computationalcomplexity,
sensitive to outliers and its results depends on initial
centroids, which are selected randomly. Many solutions have
been proposed to improve the performance of KMeans. But
no one provide a global solution. Some of proposed
algorithms are fast but they fail to maintain the quality of
clusters. Some generate clusters of good quality but they are
very expensive in term of computational complexity. The
outliers are major problem that will effect on quality of
clusters. Some algorithm only works on only numerical
datasets.
4. LITERATURE REVIEW
Amira Boukhdhir, Oussama Lachiheb , Mohamed Salah
Gouider [1] proposed algorithm an improved KMeans with
Map Reduce design for very large dataset. Thealgorithmtakes
less execution time as compared to traditional KMeans,
PKMeans and Fast KMeans. It removes the outlier from
numerical datasets also Map Reduce technique used to select
initial centroids and forming the clusters. But it has
limitations like the value of numbers of centroids required as
input by user. It works on numerical datasets only. Also
numbers of clusters are not determined automatically.
Duong Van Hieu, Phayung Meesad[2]proposedalgorithm for
reducing executing time of the k-means .They implemented
this by cutting off a number of last iterations. In this
experiment method 30% of iterations are reduced,so30%of
executing time is reduced, and accuracy is high. However, the
choosing randomly the initial centroids produces the instable
clusters. Clustering result may be affected by noise points, so
it produces inaccurate result.
Li Ma and al [3] developed a solution for improving the
quality of traditional k-means clusters. They used the
technique of selecting systematicallythevalueofki.e number
of clusters as well as the initial centroids. Also they reduced
the number of noise points so the outlier’s problem solved.
This algorithm produces good quality clusters but it takes
more computation time.
Xiaoli Cui and al [4] proposed an algorithm i. e. an improved
k-means. This algorithm works on only representativepoints
instead of the whole dataset, using a sampling technique. The
result of this the I/O cost and the network cost reduced
because of Parallel K-means. Experimentalresultsshowsthat
the algorithm is efficient and it has better performance as
compared with k-means but, there is no high accuracy.
Yugal Kumar, G. Sahoo [5] focused on K-Means initialization
problems. The K-Means initialization problemofalgorithmis
formulated by two ways; first, how many numbersofclusters
required for clustering and second, how to initialize initial
centers for clusters of K-Means algorithm. This paper covers
the solution for of the initialization problem of initial cluster
centers. For that, a binary search initialization method is
used to initialize the initial cluster points i.e. initial centroid
for K-Means algorithm Performance of algorithm evaluated
using UCI repository datasets.
Huang Xiuchang, SU Wei [6] focused on problem of user
behavior pattern analysis, which has the insensitivity of
numerical value, uneven spatial and temporal distribution
characteristics strong noise. The traditional clustering
algorithm not works properly. This paper analyses the
existing clustering methods, trajectory analysismethods,and
behavior pattern analysis methods, and combines clustering
algorithm into the trajectory analysis. After modifying the
traditional K-MEANS clustering algorithm,thenewimproved
algorithm designed which is suitable to solve the problem of
user behavior pattern analysis compared with traditional
clustering methods on the basis of the test of the simulation
data and actual data, the results shows that the improved
algorithm more suitable for solving the trajectory pattern of
user behavior problems.
Nidhi Singh, Divakar Singh [7] K-means is widely used for
clustering algorithm. This paper proves that the accuracy of
k-means for iris dataset is much than the hierarchical
clustering and for diabetes dataset accuracy of hierarchal
clustering is more than the k-means algorithm. The time
taken to cluster the data sets is less in case of k-means. A good
clustering method produces high-quality clusters to ensure
that objects of a same cluster are more similar thanmembers
of different cluster. Kmeans algorithm in this paper works
well for large datasets.
Kedar B. Sawan [8] existing K-meansclusteringalgorithm has
a number of drawbacks. The selection of initial starting point
will have effect on the results of number of clusters formed
and their new centroids. Overview of the existing methods of
choosing the value of K i.e. the number of clusters along with
new method to select the initial centroid points for the K-
means algorithm has been proposed in the paper along with
the modified K-Means algorithm to overcome the deficiency
of the classical K-means clustering algorithm. The new
method is closely related to the approach of K-means
clustering because it takesintoaccountinformationreflecting
the performance of the algorithm. The improved version of
the algorithm uses a systematic way to find initial centroid
points which reduces the number of dataset scans and will
produce better accuracy in less number of iteration with the
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 02 | Feb -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1215
traditional algorithm. The method could be computationally
expensive if used with large data sets because it requires
calculating the distance of every point with the first point of
the given dataset as a very first step of the algorithm and sort
it based on this distance. However this drawback could be
taken care by using multi-threading technique while
implementing it within the program. However further
research is required to verify the capability of this method
when applied to data sets with more complex object
distributions.
Bapusaheb B. Bhusare, S. M. Bansode [9] the K means
clustering algorithm which mainly based on initial cluster
centers. In this paper K means clustering algorithm by
designed in such way that the initial centroids selected using
Pillar algorithm. Pillar algorithmeffectivelychoosestheinitial
centroids and improves accuracy of clusters. However,
proposed algorithm has outlier problem leads to reduced
performance. So there is need to choose the appropriate
parameter in data set for outlier detection mechanism. An
improvement in pillar algorithm is done and the number of
distance calculation reduced for the previous initial centroids
neighbors and used for next step of iterations which causes
to increase in the computational time. The experimental
results show that the use of pillar algorithm with change
improved solution.
Kamaljit Kaur, Dr. Dalvinder Singh Dhaliwal, Dr. Ravinder
Kumar Vohra [10] found that the K-Means algorithm has two
major limitations 1. Several distance calculations of each data
point from all the centroids in each iteration. 2. The final
clusters depend upon the selection of initial centroids. This
work improves k-Means clustering algorithm designed in
MATLAB and the datasets from UCI machine learning
repository used. The initial centroids initial centroids not
selected randomly. By using new approach good clustering
results obtained. The new method of selection of initial
centroid is better than selectingtheinitialcentroidsrandomly.
Abhijit Kane’s [11] paper includes the automatically find the
number of clusters in a dataset. Here every step requires re-
clustering of the dataset, total O (n) operations computed.
This method works well for clusters that are distinctly
separated. This method is also density-independent,makingit
useful for clustering algorithms like the Expectation-
maximization algorithm.
Omar Kettani, Faical Ramdani, Benaissa Tadili [12] work
covers an algorithm designed for automatic clustering. This
method computes the correct number of clusters on tested
data sets. This method was compared with G-means. The
comparison of algorithm shows that the proposed approach
much better than G-means in terms of clustering accuracy.
Avni Godara, Varun Sharm [13] covers the prime algorithm.
The KMeans clustering is a powerful algorithm used most of
the application in daily life dataset, but problem of initial
centroid selection. In past years number of papers presented
to improve classical kmeansalgorithm.Toremoveproblemof
initial centroid selection need to define data points for
centroid before next iteration. The use of prim’s algorithm
gives better results for selection of initial centroidandchoose
easily data points for future iterations. Experimental result
also shows that the prime algorithm gives better and optimal
performance for initial centroids, accuracy of result not
adjusted.
D. Sharmila Rani, V. T. Shenbagamuthu [14] K-means is a
typical clustering algorithm and it is used for clustering large
sets of data. This work includes K-means algorithm and
analyses the standard K-means clustering algorithm. The
standard K-means algorithm is computationally complex and
need to reassign the data points, a number of times during
every iteration, which makes effect on the efficiency of
standard K-means clustering. This paper work covers a
simple and efficient way for assigning data points to clusters.
This work ensures that the entire process of clustering in O
(nk) time without sacrificing the accuracy of clusters.
Effat Naaz, Divya Sharma, D Sirisha, Venkatesan M. [15]
Paper build a system to know the accuracy of medication
associated with each symptom.TodothisK-meansClustering
on the clinical note corpus applied. The document clustering
results in improving the medication recommendation. An
experimental result shows that pre-processing before
clustering results in efficient process of clustering. For
experimental workdifferent toolsusedlike,sectionannotator,
symptom annotator, negation annotator and medication
annotator to get different views of clinical notes which
improves the visibility of clinical note. The result of this is
increase of the accuracy of medications associated with the
symptoms.
5. C0NCLUSION
In this review work most widely used k-means clustering
techniques of data mining is analyzed. This work shows that
there are several methods to improve the clustering with
different approaches. Various clustering techniques are
reviewed whichimprovethe existingalgorithmwith different
perspective. Some limitations of existing algorithm will be
eliminated in future work. This technique will be useful in
extraction of useful information using cluster from huge
database. It removes the limitation of K-means clustering
algorithm and gives accurate result in less time so we can say
it's very efficient than standard K-meansclusteringalgorithm
and quality of cluster also improved. From Our analysis of
different K-means approaches, we conclude that it's better
than traditional K-means clustering algorithm.
6. REFERENCES
[1] Amira Boukhdhir Oussama Lachiheb, Mohamed Sala
Gouider. “An improved Map Reduce Design of Kmeans
for clustering very large datasets”, IEEE transaction.
[2] V. Duon, M. Phayung. ”Fast K-Means Clustering for very
large datasets based on Map Reduce Combined with
New Cutting Method (FMR KMeans)”, Springer
International Publishing Switzerland, 2015.
[3] M. Li and al. “An improved k-means algorithm based on
Map reduce and Grid”, International Journal of Grid
Distribution Computing, (2015)
[4] C. Xiaoli and al. “Optimized big data K-means clustering
using Map Reduce”, Springer Science+BusinessMedia
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 02 | Feb -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1216
New York (2014).
[5] Yugal Kumar and G. Sahoo, “A New Initialization Method
to Originate Initial Cluster Centers for K-Means
Algorithm”, International Journal of Advanced Science
and Technology Vol.62, (2014).
[6] Huang Xiuchang , SU Wei ,”An Improved K-means
Clustering Algorithm“ ,JOURNAL OF NETWORKS, VOL.
9, NO. 1, JANUARY 2014
[7] Nidhi Singh, Divakar Singh,” Performance Evaluation of
K-Means and Hierarchal Clustering in Terms of
Accuracy and Running Time”, (IJCSIT) International
Journal of Computer Science and Information
Technologies, Vol. 3 (3) , 2012.
[8] Kedar B. Sawant, “Efficient Determination of Clusters in
K-Mean Algorithm Using Neighborhood Distance
“International Journal of Emerging Engineering
Research and Technology Volume 3, Issue 1, January
2015.
[9] Bapusaheb B. Bhusare, S. M. Bansode, ”Centroids
Initialization for K-Means Clustering using Improved
Pillar Algorithm” , International Journal of Advanced
Research in Computer Engineering & Technology
(IJARCET) Volume 3 Issue 4, April 2014 .
[10] Kamaljit Kaur, Dr. Dalvinder Singh Dhaliwal,Dr.Ravinder
Kumar Vohra ,”Statistically Refining the Initial Pointsfor
K-Means Clustering Algorithm “,InternationalJournal of
Advanced Research in Computer Engineering &
Technology (IJARCET) Volume 2, Issue 11, November
2013.
[11] Abhijit Kane,” Determining the number of clusters for a
Kmeans clustering algorithm”, Indian Journal of
Computer Science and Engineering (IJCSE) Vol. 3 No.5
Oct-Nov 2012
[12] Omar Kettani, Faical Ramdani, Benaissa Tadili, ”AK-
means: An Automatic Clustering Algorithm based on
Kmeans “, Journal of Advanced Computer Science &
Technology, 4 (2) (2015) .
[13] Avni Godara, Varun Sharma,” Improvement of Initial
Centroids in KmeansclusteringAlgorithm”, Vol-2Issue-2
2016 IJARIIE
[14] D. Sharmila Rani, V.T. Shenbagamuthu,”Modified K-
Means Algorithm for Initial Centroid Detection”,
International Journal of Innovative Research in
Computer and Communication Engineering (An ISO
3297: 2007 Certified Organization) Vol.2, Special Issue
1, March 2014
[15] Effat Naaz, Divya Sharma, D Sirisha, Venkatesan M,”
Enhanced Kmeans clustering approach for healthcare
analysis using clinical documents”, International
Journal of Pharmaceutical and Clinical Research 2016.

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Review of Existing Methods in K-means Clustering Algorithm

  • 1. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 02 | Feb -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1213 Review of Existing Methods in K-means Clustering Algorithm MS. Kavita Shiudkar1, Prof. Sachine Takmare2 1 ME CSE, Bharti Vidyapeeth college of Engineering, Kolhapur, Maharashtra, India 2 Assistant Professor, Dept. of CSE, Bharti Vidyapeeth college of Engineering Kolhapur, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract – Data mining is the process of extracting useful information from the large amount of data and converting it into understandable form for further use. Clustering is the process of grouping object attributes and features such that the data objects in one group are more similar than data objects inanothergroup.Butitis now very challenging due to the sharply increase in the large volume of data generated by number of applications. Kmeans is a simple and widely used algorithm for clustering data. But, the traditional k- means is computationallyexpensive;sensitivetooutlier’s i.e. unnecessary data and producesunstableresulthence it becomes inefficient when dealing with very large datasets. Solving these Issues is the subject of many recent research works. In this paper, wewill doareviewon k-means clustering algorithms. Key Words: Initial Centroids, Clustering, Data mining, Data sets, K-means clustering, Map-Reduce. 1. INTRODUCTION Big Data is evolving term that describes any voluminous amount of structured, semi-structured and unstructureddata. It is characterized by “5Vs”, volume (size of data set), variety (range of data type and source), velocity (speed of data in and out), value (how useful the data is), and veracity (quality of data). It creates challenges in their collection, processing, management and analysis. As new data and updates are constantly arriving, there is need of data mining to tackle challenges. The purpose of the data mining technique is to mine information from a bulky data set and make over it into a reasonable form for supplementary purpose. Data mining is also known as the knowledge discovery in databases (KDD). Technically, data mining is the process of finding patterns among number of fields in large relational database. It is the best process to differentiate between data and information. Data mining consists of extract, transform, and load transaction data onto the data warehouse system, Store and manage the data in a multidimensional database system, Provide data access to business analysts and information technology professionals, analyze the data by application software, Present the data in a useful format, such as a graph or table. 2. CLUSTERING It makes an important role in data analysis and data mining applications. Data divides into similarobjectgroupsbasedon their features, each data group will consist of collection of similar objects in clusters. Clustering is a process of unsupervised learning. Highly superior clusters have high intra-class similarity and low inter-class similarity. Several algorithms have been designed to perform clustering, each one uses different principle. They are divided into hierarchical, partitioning, density-based, model based algorithms and grid-based. Fig: 1 Clustering stages There are two types of Clustering Partitioning and Hierarchical Clustering. 1. Hierarchical Clustering- A set of nested clusters organized in the form of tree. 2. Partitioning Clustering - A division of data objects intosubsets(clusters)suchthateachdataobject is in exactly one subset. 3. K-MEANS CLUSTERING K-means clustering technique is widely used clustering algorithm, which is most popular clustering algorithm that is used in scientific and industrial applications.Itisamethodof cluster analysis which is used to partition N objects into k clusters in such a way that each object belongs to the cluster Raw Input Data Data Clusters Clusters Clustering Algorithms
  • 2. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 02 | Feb -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1214 with the nearest mean [3]. The Traditional KMeans algorithm is very simple [3]: 1. Select the value of K i.e. Initial centroids. 2. Repeat step 3 and 4 for all data points in dataset. 3. Find the nearest point from that centroids in the Dataset. 4. Form K cluster by assigning each point to its closest centroid. 5. Calculate the new global centroid for each cluster. Properties of k-means algorithm [3]: 1. Efficient while processing large data set. 2. It works only on numeric values. 3. The shapes of clusters are convex. K-means is the most commonly used partitioning algorithm in cluster analysis because of its simplicity and performance. But it has some restrictions when dealing with very large datasets because of high computationalcomplexity, sensitive to outliers and its results depends on initial centroids, which are selected randomly. Many solutions have been proposed to improve the performance of KMeans. But no one provide a global solution. Some of proposed algorithms are fast but they fail to maintain the quality of clusters. Some generate clusters of good quality but they are very expensive in term of computational complexity. The outliers are major problem that will effect on quality of clusters. Some algorithm only works on only numerical datasets. 4. LITERATURE REVIEW Amira Boukhdhir, Oussama Lachiheb , Mohamed Salah Gouider [1] proposed algorithm an improved KMeans with Map Reduce design for very large dataset. Thealgorithmtakes less execution time as compared to traditional KMeans, PKMeans and Fast KMeans. It removes the outlier from numerical datasets also Map Reduce technique used to select initial centroids and forming the clusters. But it has limitations like the value of numbers of centroids required as input by user. It works on numerical datasets only. Also numbers of clusters are not determined automatically. Duong Van Hieu, Phayung Meesad[2]proposedalgorithm for reducing executing time of the k-means .They implemented this by cutting off a number of last iterations. In this experiment method 30% of iterations are reduced,so30%of executing time is reduced, and accuracy is high. However, the choosing randomly the initial centroids produces the instable clusters. Clustering result may be affected by noise points, so it produces inaccurate result. Li Ma and al [3] developed a solution for improving the quality of traditional k-means clusters. They used the technique of selecting systematicallythevalueofki.e number of clusters as well as the initial centroids. Also they reduced the number of noise points so the outlier’s problem solved. This algorithm produces good quality clusters but it takes more computation time. Xiaoli Cui and al [4] proposed an algorithm i. e. an improved k-means. This algorithm works on only representativepoints instead of the whole dataset, using a sampling technique. The result of this the I/O cost and the network cost reduced because of Parallel K-means. Experimentalresultsshowsthat the algorithm is efficient and it has better performance as compared with k-means but, there is no high accuracy. Yugal Kumar, G. Sahoo [5] focused on K-Means initialization problems. The K-Means initialization problemofalgorithmis formulated by two ways; first, how many numbersofclusters required for clustering and second, how to initialize initial centers for clusters of K-Means algorithm. This paper covers the solution for of the initialization problem of initial cluster centers. For that, a binary search initialization method is used to initialize the initial cluster points i.e. initial centroid for K-Means algorithm Performance of algorithm evaluated using UCI repository datasets. Huang Xiuchang, SU Wei [6] focused on problem of user behavior pattern analysis, which has the insensitivity of numerical value, uneven spatial and temporal distribution characteristics strong noise. The traditional clustering algorithm not works properly. This paper analyses the existing clustering methods, trajectory analysismethods,and behavior pattern analysis methods, and combines clustering algorithm into the trajectory analysis. After modifying the traditional K-MEANS clustering algorithm,thenewimproved algorithm designed which is suitable to solve the problem of user behavior pattern analysis compared with traditional clustering methods on the basis of the test of the simulation data and actual data, the results shows that the improved algorithm more suitable for solving the trajectory pattern of user behavior problems. Nidhi Singh, Divakar Singh [7] K-means is widely used for clustering algorithm. This paper proves that the accuracy of k-means for iris dataset is much than the hierarchical clustering and for diabetes dataset accuracy of hierarchal clustering is more than the k-means algorithm. The time taken to cluster the data sets is less in case of k-means. A good clustering method produces high-quality clusters to ensure that objects of a same cluster are more similar thanmembers of different cluster. Kmeans algorithm in this paper works well for large datasets. Kedar B. Sawan [8] existing K-meansclusteringalgorithm has a number of drawbacks. The selection of initial starting point will have effect on the results of number of clusters formed and their new centroids. Overview of the existing methods of choosing the value of K i.e. the number of clusters along with new method to select the initial centroid points for the K- means algorithm has been proposed in the paper along with the modified K-Means algorithm to overcome the deficiency of the classical K-means clustering algorithm. The new method is closely related to the approach of K-means clustering because it takesintoaccountinformationreflecting the performance of the algorithm. The improved version of the algorithm uses a systematic way to find initial centroid points which reduces the number of dataset scans and will produce better accuracy in less number of iteration with the
  • 3. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 02 | Feb -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1215 traditional algorithm. The method could be computationally expensive if used with large data sets because it requires calculating the distance of every point with the first point of the given dataset as a very first step of the algorithm and sort it based on this distance. However this drawback could be taken care by using multi-threading technique while implementing it within the program. However further research is required to verify the capability of this method when applied to data sets with more complex object distributions. Bapusaheb B. Bhusare, S. M. Bansode [9] the K means clustering algorithm which mainly based on initial cluster centers. In this paper K means clustering algorithm by designed in such way that the initial centroids selected using Pillar algorithm. Pillar algorithmeffectivelychoosestheinitial centroids and improves accuracy of clusters. However, proposed algorithm has outlier problem leads to reduced performance. So there is need to choose the appropriate parameter in data set for outlier detection mechanism. An improvement in pillar algorithm is done and the number of distance calculation reduced for the previous initial centroids neighbors and used for next step of iterations which causes to increase in the computational time. The experimental results show that the use of pillar algorithm with change improved solution. Kamaljit Kaur, Dr. Dalvinder Singh Dhaliwal, Dr. Ravinder Kumar Vohra [10] found that the K-Means algorithm has two major limitations 1. Several distance calculations of each data point from all the centroids in each iteration. 2. The final clusters depend upon the selection of initial centroids. This work improves k-Means clustering algorithm designed in MATLAB and the datasets from UCI machine learning repository used. The initial centroids initial centroids not selected randomly. By using new approach good clustering results obtained. The new method of selection of initial centroid is better than selectingtheinitialcentroidsrandomly. Abhijit Kane’s [11] paper includes the automatically find the number of clusters in a dataset. Here every step requires re- clustering of the dataset, total O (n) operations computed. This method works well for clusters that are distinctly separated. This method is also density-independent,makingit useful for clustering algorithms like the Expectation- maximization algorithm. Omar Kettani, Faical Ramdani, Benaissa Tadili [12] work covers an algorithm designed for automatic clustering. This method computes the correct number of clusters on tested data sets. This method was compared with G-means. The comparison of algorithm shows that the proposed approach much better than G-means in terms of clustering accuracy. Avni Godara, Varun Sharm [13] covers the prime algorithm. The KMeans clustering is a powerful algorithm used most of the application in daily life dataset, but problem of initial centroid selection. In past years number of papers presented to improve classical kmeansalgorithm.Toremoveproblemof initial centroid selection need to define data points for centroid before next iteration. The use of prim’s algorithm gives better results for selection of initial centroidandchoose easily data points for future iterations. Experimental result also shows that the prime algorithm gives better and optimal performance for initial centroids, accuracy of result not adjusted. D. Sharmila Rani, V. T. Shenbagamuthu [14] K-means is a typical clustering algorithm and it is used for clustering large sets of data. This work includes K-means algorithm and analyses the standard K-means clustering algorithm. The standard K-means algorithm is computationally complex and need to reassign the data points, a number of times during every iteration, which makes effect on the efficiency of standard K-means clustering. This paper work covers a simple and efficient way for assigning data points to clusters. This work ensures that the entire process of clustering in O (nk) time without sacrificing the accuracy of clusters. Effat Naaz, Divya Sharma, D Sirisha, Venkatesan M. [15] Paper build a system to know the accuracy of medication associated with each symptom.TodothisK-meansClustering on the clinical note corpus applied. The document clustering results in improving the medication recommendation. An experimental result shows that pre-processing before clustering results in efficient process of clustering. For experimental workdifferent toolsusedlike,sectionannotator, symptom annotator, negation annotator and medication annotator to get different views of clinical notes which improves the visibility of clinical note. The result of this is increase of the accuracy of medications associated with the symptoms. 5. C0NCLUSION In this review work most widely used k-means clustering techniques of data mining is analyzed. This work shows that there are several methods to improve the clustering with different approaches. Various clustering techniques are reviewed whichimprovethe existingalgorithmwith different perspective. Some limitations of existing algorithm will be eliminated in future work. This technique will be useful in extraction of useful information using cluster from huge database. It removes the limitation of K-means clustering algorithm and gives accurate result in less time so we can say it's very efficient than standard K-meansclusteringalgorithm and quality of cluster also improved. From Our analysis of different K-means approaches, we conclude that it's better than traditional K-means clustering algorithm. 6. REFERENCES [1] Amira Boukhdhir Oussama Lachiheb, Mohamed Sala Gouider. “An improved Map Reduce Design of Kmeans for clustering very large datasets”, IEEE transaction. [2] V. Duon, M. Phayung. ”Fast K-Means Clustering for very large datasets based on Map Reduce Combined with New Cutting Method (FMR KMeans)”, Springer International Publishing Switzerland, 2015. [3] M. Li and al. “An improved k-means algorithm based on Map reduce and Grid”, International Journal of Grid Distribution Computing, (2015) [4] C. Xiaoli and al. “Optimized big data K-means clustering using Map Reduce”, Springer Science+BusinessMedia
  • 4. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 02 | Feb -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1216 New York (2014). [5] Yugal Kumar and G. Sahoo, “A New Initialization Method to Originate Initial Cluster Centers for K-Means Algorithm”, International Journal of Advanced Science and Technology Vol.62, (2014). [6] Huang Xiuchang , SU Wei ,”An Improved K-means Clustering Algorithm“ ,JOURNAL OF NETWORKS, VOL. 9, NO. 1, JANUARY 2014 [7] Nidhi Singh, Divakar Singh,” Performance Evaluation of K-Means and Hierarchal Clustering in Terms of Accuracy and Running Time”, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 3 (3) , 2012. [8] Kedar B. Sawant, “Efficient Determination of Clusters in K-Mean Algorithm Using Neighborhood Distance “International Journal of Emerging Engineering Research and Technology Volume 3, Issue 1, January 2015. [9] Bapusaheb B. Bhusare, S. M. Bansode, ”Centroids Initialization for K-Means Clustering using Improved Pillar Algorithm” , International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 3 Issue 4, April 2014 . [10] Kamaljit Kaur, Dr. Dalvinder Singh Dhaliwal,Dr.Ravinder Kumar Vohra ,”Statistically Refining the Initial Pointsfor K-Means Clustering Algorithm “,InternationalJournal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 2, Issue 11, November 2013. [11] Abhijit Kane,” Determining the number of clusters for a Kmeans clustering algorithm”, Indian Journal of Computer Science and Engineering (IJCSE) Vol. 3 No.5 Oct-Nov 2012 [12] Omar Kettani, Faical Ramdani, Benaissa Tadili, ”AK- means: An Automatic Clustering Algorithm based on Kmeans “, Journal of Advanced Computer Science & Technology, 4 (2) (2015) . [13] Avni Godara, Varun Sharma,” Improvement of Initial Centroids in KmeansclusteringAlgorithm”, Vol-2Issue-2 2016 IJARIIE [14] D. Sharmila Rani, V.T. Shenbagamuthu,”Modified K- Means Algorithm for Initial Centroid Detection”, International Journal of Innovative Research in Computer and Communication Engineering (An ISO 3297: 2007 Certified Organization) Vol.2, Special Issue 1, March 2014 [15] Effat Naaz, Divya Sharma, D Sirisha, Venkatesan M,” Enhanced Kmeans clustering approach for healthcare analysis using clinical documents”, International Journal of Pharmaceutical and Clinical Research 2016.