Call for Paper - May 2023 Edition
IJCA solicits original research papers for the May 2023 Edition. Last date of manuscript submission is April 20, 2023. Read More

Assessment of Financial Status of SHG Members: A Clustering Approach

International Journal of Computer Applications
© 2011 by IJCA Journal
Number 1 - Article 1
Year of Publication: 2011
Sajeev B. U
K. Thangavel

Sajeev B U and K Thangavel. Article: Assessment of Financial Status of SHG Members: A Clustering Approach. International Journal of Computer Applications 32(2):7-15, October 2011. Full text available. BibTeX

	author = {Sajeev B. U and K. Thangavel},
	title = {Article: Assessment of Financial Status of SHG Members: A Clustering Approach},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {32},
	number = {2},
	pages = {7-15},
	month = {October},
	note = {Full text available}


Data mining has attracted a great deal of attention in information industry and in society as a whole in recent years, due to the wide availability of huge amount of data and for converting these data to useful information and knowledge. Clustering analysis is a key and easy tool in data mining and pattern recognition. In this paper K-Means and Fuzzy C-means clustering algorithms are used for evaluating the performance of various Self Help Groups (SHGs) in Kerala State, and suggestions are made to improve socioeconomic status. The necessary information about the members of SHG has been collected from 9 districts in Kerala State, Indi. The parameters chosen for the study are financial status, types of loan availed, improvement in assets before and after joining the group, effect of joining in more than one group. District wise analysis are also performed.


  • Frawley,W.,G. Piatetsky-Shapiro and C. Matheus, 1992. Knowledge Discovery in Databases: An Overview. AI Magazine, pp: 213-228.
  • Hand, D., H. Mannila and P. Smyth. 2001.Principles of Data Mining. MIT Press, Cambridge, MA.
  • Chen, Y.L., J.M. Chen and C.W. Tung, 2007. A data mining approach for retail knowledge discovery with consideration of the effect of shelf-space adjacency on sales. Decision Support Systems, 42: 1503-1520..
  • Romero, C. and S. Ventura. 2007.Educational data mining: A survey from 1995 to 2005. Expert Systems with Application”s, 33: 135-146.
  • Lee, A.J.T., R.W. Hong, W.M. Ko, W.K. Tsaoand H.H. Lin.2007.Mining spatial association rules in image databases. Information Science. 177: 1593-1608. 2007.
  • Maran, U., S. Sild, I. Kahn and K. Takkis, 2007. Mining of the chemical information in GRID environment. Future Generation Computer Systems,23, 76-83.
  • Yang, Q., J. Yin, C. Ling and R. Pan. 2007.Extracting actionable knowledge from decision trees. IEEE Transactions on Knowledge and Data Engineering, 19, 43-55.
  • Imamura, T., S. Matsumoto, Y. Kanagawa, B. Tajima, S. Matsuya, M. Furue and H. Oyama.2007. A technique for identifying three diagnostic findings using association analysis. Medical and Biological Engineering and Computing ,45, 51-59.
  • The MathWorks, Inc. textbook online,
  • A. Jain, M. N. Murty and P. J. Flynn, 1999.Data Clustering: A Review”, ACM Computing Surveys, Vol. 31(3), pp. 264-323.
  • J. Hartigan.1975. Clustering Algorithms. New York: Wiley.
  • B. Kumar.2005.Impact of Microfinance through SHG-Bank Linkage in India: A Micro Study. Vilakshan, XIMB Journal of Management, July 9,.
  • Mahendra Varman P; 2005. Impact of Self-Help Groups on Formal Banking Habits; Economic and Political Weekly April 23, pp 1705-13.
  • B. Narayana swamy, K. Narayana Gowda and G. N. Nagaraj .2007.Performance of Self Help Groups of Karnataka in Farm Activities”; Karnataka J. Agric. Sci.,20(1):85 - 88.
  • J. Pena, J. Lozano, and P. Larranaga, 1999. An Empirical Comparison Of Four Initialization Methods For The K-Means Algorithm. Pattern Recognition Letters, Vol. 20 No. 10, pp. 1027-10.
  • J. Macqueen, 1967.Some Methods For Classification And Analysis Of Multivariate Observations. In proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, University of California Press, pp. 281-297.
  • Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Text Book on Introduction to Data Mining” Pearson Education; PP 496-97; III edition.
  • J.C. Bezdek, 1981. Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum, New York.
  • J.C. Dunn, 1974. A fuzzy relative of the ISODATA process and its use in detecting compact, well-separated clusters, J. Cybernet. 3, 32–57.
  • I. Gath, A.B. Geva, 1989. Unsupervised optimal fuzzy clustering, IEEE Trans. Pattern Anal. Mach. Intell. 11 773–781.
  • R.N. Dave, 1990. Fuzzy-shell clustering and applications to circle detection in digital images, Int. J. General Syst. 16. 343–355.
  • M.S. Yang, C.H. Ko, 1997. On cluster-wise fuzzy regression analysis, IEEE Trans. Systems, Man, Cybern. 27, 1–13
  • Kuo-Lung Wu, Miin-Shen Yang; 2002. Alternative C-Means clustering algorithms, Pattern Recognition 35, 2267 – 2278
  • H. Galhardas, D. Florescu, D. Shasha, and E. Simon. 2000, Ajax: An Extensible Data Cleaning Tool, Proc. ACM SIGMOD Int’l Conf.Management of Data,.
  • H. Galhardas, D. Florescu, D. Shasha, E. Simon, and C. Saita, 2001 .Declarative Data Cleaning: Language, Model, and Algorithms, roc. 2001 Very Large Data Bases (VLDB) Conf.
  • M. Hernandez and S. Stolfo, 1995.The Merge/Purge Problem for Large Databases,” Proc. ACM SIGMOD Int’l Conf. Management of Data, pp. 127-138, May..
  • M.L. Lee, T.W. Ling, and W.L. Low, 2000. Intelliclean: A Knowledge- Based Intelligent Data Cleaner. Proc. Sixth ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining,.
  • L. Yu and H. Liu. 2003 . Feature selection for high-dimensional data: a fast correlation-based filter solution”. In: Proceedings of the 20th International Conferences on Machine Learning, Washington DC.
  • R. Kohavi and G. John. 1997 .Wrappers for feature subset selection.Artificial Intelligence. Vol. (1-2), pp. 273-324.