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A Survey on Data Clustering Algorithms


Affiliations
1 Department in Computer Technology, Dr. SNS Rajalakshmi College of Arts and Science, Tamil Nadu, India
2 Dr. SNS Rajalakshmi College of Arts and Science, Tamil Nadu, India
     

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Clustering is a technique adapted in many real world applications. Generally clustering can be thought of as partitioning the data into group or subsets, which contain analogous objects. A lot of clustering techniques like K-Means algorithm, Fuzzy C-Means algorithm (FCM), spectral clustering algorithm and so on has been proposed earlier in literature. Recently, clustering algorithms are extensively used for mixed data types to evaluate the performance of the clustering techniques. This paper presents a survey on various clustering algorithms that are proposed earlier in literature. Moreover it provides an insight into the advantages and limitations of some of those earlier proposed clustering techniques. The comparison of various clustering techniques is provided in this paper. The future enhancement section of this paper provides a general idea for improving the existing clustering algorithms to achieve better clustering accuracy.


Keywords

Artificial Intelligence, Clustering, Mixed Dataset, Learning Algorithm, Image Processing.
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  • A Survey on Data Clustering Algorithms

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Authors

N. Kamalraj
Department in Computer Technology, Dr. SNS Rajalakshmi College of Arts and Science, Tamil Nadu, India
V. Shobana
Dr. SNS Rajalakshmi College of Arts and Science, Tamil Nadu, India

Abstract


Clustering is a technique adapted in many real world applications. Generally clustering can be thought of as partitioning the data into group or subsets, which contain analogous objects. A lot of clustering techniques like K-Means algorithm, Fuzzy C-Means algorithm (FCM), spectral clustering algorithm and so on has been proposed earlier in literature. Recently, clustering algorithms are extensively used for mixed data types to evaluate the performance of the clustering techniques. This paper presents a survey on various clustering algorithms that are proposed earlier in literature. Moreover it provides an insight into the advantages and limitations of some of those earlier proposed clustering techniques. The comparison of various clustering techniques is provided in this paper. The future enhancement section of this paper provides a general idea for improving the existing clustering algorithms to achieve better clustering accuracy.


Keywords


Artificial Intelligence, Clustering, Mixed Dataset, Learning Algorithm, Image Processing.