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Approaches to Partition Medical Data using Clustering Algorithms

by P. Kalyani
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 49 - Number 23
Year of Publication: 2012
Authors: P. Kalyani
10.5120/7941-1102

P. Kalyani . Approaches to Partition Medical Data using Clustering Algorithms. International Journal of Computer Applications. 49, 23 ( July 2012), 7-10. DOI=10.5120/7941-1102

@article{ 10.5120/7941-1102,
author = { P. Kalyani },
title = { Approaches to Partition Medical Data using Clustering Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { July 2012 },
volume = { 49 },
number = { 23 },
month = { July },
year = { 2012 },
issn = { 0975-8887 },
pages = { 7-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume49/number23/7941-1102/ },
doi = { 10.5120/7941-1102 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:46:57.243657+05:30
%A P. Kalyani
%T Approaches to Partition Medical Data using Clustering Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 49
%N 23
%P 7-10
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The successful application of data mining in fields like e-business, marketing and retail have led to the popularity of its use in knowledge discovery in databases (KDD) in other industries and sectors. Data is a great asset to meet long-term goals of any organization and can help to improve customer relationship management. It can also benefit healthcare providers like hospitals, clinics and physicians, and patients, for example, by identifying effective treatments and best practices popularity of its use in knowledge discovery in databases (KDD) in other industries and sectors. Efficient clustering tools reduce demand on costly healthcare resources. It can help physicians cope with the information overload and can assist in future planning for improved services. Clustering results are used to study independence or correlation between diseases and for better insight into medical survey data. To achieve this, create clustering algorithms that enhances the traditional K-Means, DB-Scan and Fuzzy C-Means algorithms.

References
  1. Christy, T. (1997) Analytical tools help health firms fight fraud. Insurance & Technology, 22(3), 22-26.
  2. Ester, M. , Kriegel, H. , Sander, J. and Xu, X. (1996) A density-based algorithm for discovering clusters in large spatial databases with noise, Proc. KDD 96, Pp. 226–231.
  3. Gillespie, G. (2000) There's gold in them thar' databases. Health Data Management, 8(11), 40-52.
  4. Halkidi, M. , Batistakis, Y. and Vazirgiannis, M. (2001) Clustering algorithms and validity measures, Proceedings of SSDBM Conference, Virginia, USA.
  5. Hamerly, G. and Elkan, C. (2003) Learning the k in k-means, Proceedings of the 17th Annual Conference on Neural Information Processing Systems, Pp. 281-288.
  6. Han, J. and Kamber, M. (2000) Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers.
  7. http://archive. ics. uci. edu/ml/datasets. html
Index Terms

Computer Science
Information Sciences

Keywords

Knowledge discovery cluster K-means Density based scan

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