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Improving Accuracy using different Data Mining Algorithms

by Pooja Pandey, Ishpreet Singh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 150 - Number 10
Year of Publication: 2016
Authors: Pooja Pandey, Ishpreet Singh
10.5120/ijca2016911573

Pooja Pandey, Ishpreet Singh . Improving Accuracy using different Data Mining Algorithms. International Journal of Computer Applications. 150, 10 ( Sep 2016), 10-13. DOI=10.5120/ijca2016911573

@article{ 10.5120/ijca2016911573,
author = { Pooja Pandey, Ishpreet Singh },
title = { Improving Accuracy using different Data Mining Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2016 },
volume = { 150 },
number = { 10 },
month = { Sep },
year = { 2016 },
issn = { 0975-8887 },
pages = { 10-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume150/number10/26128-2016911573/ },
doi = { 10.5120/ijca2016911573 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:55:36.394540+05:30
%A Pooja Pandey
%A Ishpreet Singh
%T Improving Accuracy using different Data Mining Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 150
%N 10
%P 10-13
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Mining large data set is an important issue to deal with as data is growing as the field grows. Today, crime rate is a menace that each country faces. With the increase in crime rate the data is increasing and it is such a critical field that accuracy is important at the same time. This paper shows the comparison in the results between clustering and the classification. K means is used in clustering and in classification decision tree is used. The process of applying decision tree and clustering one after the other is used CDDT(clustered data of decision tree) in this paper.

References
  1. Rajeswari, K., Acharya, O., Sharma, M., Kopnar,M., & Karandikar, K.” Improvement in k-Means Clustering Algorithm Using Data Clustering”, In Computing Communication Control and Automation (ICCUBEA), 2015 International Conference on,vol.3, no.15, pp. 367-369, IEEE.
  2. Research on k-means Clustering Algorithm An Improved k-means Clustering Algorithm Shi Na College of Information Engineering, Capital Normal
  3. Lima, M. F., Zarpelao, B. B., Sampaio, L. D., Rodrigues, J. J., Abrao, T., & Proença Jr, M. L.” Detection using baseline and K-means clustering”, In Software, Telecommunications and Computer Networks (softcom), 2010International Conference on, vol.3, no.5 pp. 305-309, IEEE.
  4. Ren, Q., & Zhuo, X. “ Application of an improved k-means algorithm in gene expression data analysis” In Systems Biology (ISB), 2011 International Conference on, pp. 87-91, IEEE.
  5. Z. Pawlak “Information systems - theoretical foundations”, Information Systems Journal 1981, Vol. 6, pp.205-218
  6. Y. Qiao, K. Zhong, H.-A.Wang and X. Li, “Developing event-condition-action rules in real-time active database”, Proceedings of the 2007 ACM symposium on Applied computing, ACM, New York, pp.511-516
  7. Z.W. Ra´s, A. Dardzi´nska, “Action rules discovery, a new simplified strategy, Foundations of Intelligent Systems”, 2006 LNAI, No. 4203, Springer, pp.445-453
  8. Z.W. Ra´s, A. Tzacheva, L.-S. Tsay, O. G¨urdal, “Mining for interesting action rules”, 2005 Proceedings of IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2005), Compiegne University of Technology, France, 2005, pp.187-193
  9. Wang, H., Qi, J., Zheng, W., & Wang, M. “Balance K-means algorithm. In Computational Intelligence and Software Engineering,” Cise 2009 International Conference on, pp. 1-3, IEEE.
  10. Esteves, R. M., Hacker, T., & Rong, C. “Competitive k-means, a new accurate and distributed k-means algorithm for large datasets” In Cloud Computing Technology and Science (cloudcom), 2013 IEEE 5th International Conference on ,Vol. 1, pp. 17-24, IEEE.
  11. Tian, L., & Jianwen, W. “Research on network intrusion detection system based on improved k-means clustering algorithm”, In computer Science-Technology and Applications, 2009. IFCSTA'09. International Forum on Vol. 1, pp. 76-79, IEEE.
  12. Singh, G., Antony, D. A., & Leavline, E. J” Data mining in network security-techniques & tools: a research perspective”, Journal of theoretical & applied
  13. Yang, Q., & Wu, X. “10 challenging problems in data mining research” International Journal of InformationTechnology & Decision Making”, vol.5, no.4,pp.597-604.
  14. Chen, C. H., Tseng, V. S., & Hong, T. P. ,”Cluster-based evaluation in fuzzy-genetic data mining. Fuzzy Systems”, IEEE Transactions on, vol. 1, no.16,pp. 249-262.
  15. Liao, S. H., Chu, P. H., & Hsiao, P. Y,” Data miningtechniques and applications–A decade review from 2000 to 2011”, Expert Systems with Applications, vol.12,no.39, pp.11303-11311.
  16. Balabantaray, R. C., Sarma, C., & Jha, M. (2015).Document Clustering using K-Means and K Medoids.Arxiv preprint arxiv:1502.07938.
  17. Sujatha, M. S., & Sona, M. A. S.,”New fast k-means clustering algorithm using modified centroid selection method”, Ininternational Journal of EngineeringResearch and Technology ,Vol. 2, No. 2 ,February-2013.
  18. Brar, R., & Sharma, N., “A NovelDensity Based KMeans Clustering Algorithm for Intrusion Detection”, Journal of Network Communications and EmergingTechnologies (JNCET) www. Jncet. Org, vol.3, no.7
  19. W. Zhao, H. Ma, and Q. He, “Parallel K-Means Clustering Based on MapReduce,” vol. 5931, Springer Berlin / Heidelberg, 2009, pp. 674– 679.
  20. B. Bahmani, B. Moseley, A. Vattani, R. Kumar, and S. Vassilvitskii, “Scalable k-means++,” Proc. VLDB Endow., vol. 5, no. 7, pp. 622–633, 2012.
  21. M.V.B.T.Santhi,V.R.N.S.S.V.SaiLeela,P. U.Anitha,D.Nagamalleswari” Enhancing K-Means Clustering Algorithm” International Journal on Computer Science And Technology(IJCST) Vol 2,Issue 4,Oct-Dec 2011
  22. Z.W. Ra´s, A. Wieczorkowska, “Action-Rules: How to increase profit of a company,in Principles of Data Mining and Knowledge Discovery”,(2000) Proceedings of PKDD 2000, Lyon, France, LNAI, No. 1910, Springer, pp.587-592
  23. Z. Ra´s, E. Wyrzykowska, H. Wasyluk,“ARAS: Action rules discovery based on agglomerative strategy, in Mining Complex Data”, Post-Proceedings of 2007 ECML/PKDD Third International Workshop (MCD 2007), LNAI, Vol. 4944, Springer, 2008, pp.196-208
  24. L.-S. Tsay, Z.W. Ra´s (2006), “Action rules discovery system DEAR3, in Foundations of Intelligent Systems”, Proceedings of ISMIS 2006, Bari, Italy, LNAI, No. 4203, Springer, pp.483-492
Index Terms

Computer Science
Information Sciences

Keywords

CDDT clustering classification decision tree

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