Mody University International Journal of Computing and Engineering Research

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A Survey on K-Means Clustering in Various Domains


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1 Department of Computer Science and Engineering, Mody Univesity of Science and Technology, Lakshmangarh, Sikar, Rajasthan, India
 

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Data mining is used to extract the hidden patterns from large datasets and extracted patterns are helpful to identify knowledge about data to users. As various approaches are there for data mining named Clustering, Classification, Association rule mining, etc. Amongst all we consider clustering, which is an unsupervised learning and grouping. This paper demonstrates clustering technique named k-means clustering and its various improvements in different domains exterminate the limitations of traditional k-means clustering. K-means clustering is the simple partitioning clustering algorithm and exhibit many limitations, so it is very important to understand various enhancements for constructing hybrid algorithms to improve accuracy of algorithms. Various areas are defined where k-means clustering is widely used nowadays such as in healthcare, improving academic performance and optimization of search engine and much more.

Keywords

Academics, Clustering, Data Mining, Healthcare, K-Means, Search Engine.
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  • A Survey on K-Means Clustering in Various Domains

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Authors

Shivangi Singla
Department of Computer Science and Engineering, Mody Univesity of Science and Technology, Lakshmangarh, Sikar, Rajasthan, India
Pinaki Ghosh
Department of Computer Science and Engineering, Mody Univesity of Science and Technology, Lakshmangarh, Sikar, Rajasthan, India

Abstract


Data mining is used to extract the hidden patterns from large datasets and extracted patterns are helpful to identify knowledge about data to users. As various approaches are there for data mining named Clustering, Classification, Association rule mining, etc. Amongst all we consider clustering, which is an unsupervised learning and grouping. This paper demonstrates clustering technique named k-means clustering and its various improvements in different domains exterminate the limitations of traditional k-means clustering. K-means clustering is the simple partitioning clustering algorithm and exhibit many limitations, so it is very important to understand various enhancements for constructing hybrid algorithms to improve accuracy of algorithms. Various areas are defined where k-means clustering is widely used nowadays such as in healthcare, improving academic performance and optimization of search engine and much more.

Keywords


Academics, Clustering, Data Mining, Healthcare, K-Means, Search Engine.

References