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Clustering Techniques in Data Mining For Improving Software Architecture: A Review

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Parneet Kaur, Kamaljit Kaur

Parneet Kaur and Kamaljit Kaur. Article: Clustering Techniques in Data Mining For Improving Software Architecture: A Review. International Journal of Computer Applications 139(9):35-39, April 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Parneet Kaur and Kamaljit Kaur},
	title = {Article: Clustering Techniques in Data Mining For Improving Software Architecture: A Review},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {139},
	number = {9},
	pages = {35-39},
	month = {April},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


Data mining is a set of problem solving skills, instructions and methods applied upon variety of domains to discover and create useful systems that are used to solve practical problems. Clustering technique defines classes and put objects which are related to them in one class on the other hand in classification objects are placed in predefined classes. There are many clustering techniques for the improvement of architecture which are discussed in this paper. This paper also gives comparative study of clustering techniques and addresses benefits and limitations of clustering techniques.


  1. Lingming Zhang, Ji Zhou, Dan Hao, Lu Zhang, Hong Mei” Prioritizing JUnit Test Cases in Absence of Coverage Information” IEEE 2009.
  2. Paolo Tonella, Paolo Avesani, Angelo Susi” Using the Case-Based Ranking Methodology for Test Case Prioritization”. 22nd IEEE International Conference on Software Maintenance (ICSM'06), 2009.
  3. Zheng Li, Mark Harman, and Robert M. Hierons” Search Algorithms for Regression Test Case Prioritization” IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, VOL. 33, NO. 4, APRIL 2007.
  4. Amar Singh and Navjot Kaur, “To Improve the Convergence Rate of K-Means Clustering Over K-Means with Weighted Page Rank Algorithm,” International journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 8, August 2012.
  5. K. A. Abdul Nazeer, M. P. Sebastian, “Improving the Accuracy and Efficiency of the k-means Clustering Algorithm, Proceedings of the World Congress on Engineering , Vol IWCE 2009, July 1 - 3, 2009, London, U.K.
  6. Batagelj.V, Mrvar.A, and Zaversnik.M, “Partitioning approaches to clustering in graphs,” Pr Drawing’1999, LNCS, 2000, pp. 90-97.
  7. Ertoz, L., Steinbach, M., and Kumar, V., “Finding clusters of different sizes, shapes, and densities dimensional data”, In Proc. of SIAM DM’03.
  8. Ester, M., Krieger, H.P., Sander,J., and Xu, X., “ A density-based algorithm for discovering clusters databases with noise”, in Proc. of 2nd Int. Conf. on Knowledge Discovery and Data Mining(KDD-96),AAAI Press, 1996, pp. 226-231.
  9. Fayyad, U. and Grinstein,G., “Information Visualization in Data Mining and Knowledge Discovery”, M 2001, pp. 182-190.
  10. Han, J., Kamber, M., and Tung, A. K. H., “Spatial clustering methods in (eds.), Geographic Data Mining and Knowledge Discovery, Taylor and Francis, 2001.
  11. Harel, D. and Koren, Y., “Clustering spatial data using random walks”, In Proc. 7th and Data Mining(KDD-2001),ACM Press, New York, pp. 281-286.
  12. Satoshi Takumi and Sadaaki Miyamoto, “Top-down vs Bottom-up methods of Linkage for Asymmetric Agglomerative Hierarchical Clustering”, International Conference on granular Computing, 2012.
  13. Kiran Agrawal, Ashish Mishra, "Improved K-MEAN Clustering Approach for Web Usage Mining", ICETET, 2009, Emerging Trends in Engineering & Technology, International Conference on, Emerging Trends in Engineering & Technology, International Conference on 2009, pp. 298-300.
  14. Rudolf Scitovski, Tomislav Marošević, “Multiple circle detection based on center-based clustering, Pattern Recognition Letters,” Volume 52, 15 January 2015, Pages 9-16, ISSN 0167-8655.
  15. José Castro, Antonio Punzón, Graham J. Pierce, Manuel Marín, Esther Abad, Identification of métiers of the Northern Spanish coastal bottom pair trawl fleet by using the partitioning method CLARA, Fisheries Research, Volume 102, Issues 1–2, February 2010, Pages 184-190, ISSN 0165-7836.
  16. Xiaoyun Chen, Sha Liu, Tao Chen, Zhengquan Zhang, Hairong Zhang, An Improved Semi-Supervised Clustering Algorithm for Multi-Density Datasets with Fewer Constraints, Procedia Engineering, Volume 29, 2012, Pages 4325-4329, ISSN 1877-7058.
  17. Chun-Chieh Chen, Ming-Syan Chen, HiClus: Highly Scalable Density-based Clustering with Heterogeneous Cloud, Procedia Computer Science, Volume 53, 2015, Pages 149-157, ISSN 1877-0509.
  18. B. Bernábe-Loranca, R. Gonzalez-Velázquez, E. Olivares-Benítez, J. Ruiz-Vanoye, J. Martínez-Flores, Extensions to K-Medoids with Balance Restrictions over the Cardinality of the Partitions, Journal of Applied Research and Technology, Volume 12, Issue 3, June 2014, Pages 396-408, ISSN 1665-6423.
  19. N. ChitraDevi, V. Palanisamy, K. Baskaran, S. Prabeela, A Novel Distance for Clustering to Support Mixed Data Attributes and Promote Data Reliability and Network Lifetime in Large Scale Wireless Sensor Networks, Procedia Engineering, Volume 30, 2012, Pages 669-677, ISSN 1877-7058.
  20. Eman Abdel-Maksoud, Mohammed Elmogy, Rashid Al-Awadi, Brain tumor segmentation based on a hybrid clustering technique, Egyptian Informatics Journal, Volume 16, Issue 1, March 2015, Pages 71-81, ISSN 1110-8665.
  21. Chrysi Laspidou, Elpiniki Papageorgiou, Konstantinos Kokkinos, Sambit Sahu, Arpit Gupta, Leandros Tassiulas, Exploring Patterns in Water Consumption by Clustering, Procedia Engineering, Volume 119, 2015, Pages 1439-1446, ISSN 1877-7058.
  22. Muhammad Usman, Imas Sukaesih Sitanggang, Lailan Syaufina, Hotspot Distribution Analyses Based on Peat Characteristics Using Density-based Spatial Clustering, Procedia Environmental Sciences, Volume 24, 2015, Pages 132-140, ISSN 1878-0296.
  23. Md Anisur Rahman, Md Zahidul Islam, Terry Bossomaier, ModEx and Seed-Detective: Two novel techniques for high quality clustering by using good initial seeds in K-Means, Journal of King Saud University - Computer and Information Sciences, Volume 27, Issue 2, April 2015, Pages 113-128, ISSN 1319-1578.


Clustering, Software Engineering, k-means, Outliers.