Assessment of Financial Status of SHG Members: A Clustering Approach

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 KMeans 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. General Terms: Data mining, Clustering, Self Help Groups

[1]  Miin-Shen Yang,et al.  Alternative c-means clustering algorithms , 2002, Pattern Recognit..

[2]  Dennis Shasha,et al.  Declarative Data Cleaning: Language, Model, and Algorithms , 2001, VLDB.

[3]  Uko Maran,et al.  Mining of the chemical information in GRID environment , 2007, Future Gener. Comput. Syst..

[4]  Marco Furini,et al.  International Journal of Computer and Applications , 2010 .

[5]  Tok Wang Ling,et al.  IntelliClean: a knowledge-based intelligent data cleaner , 2000, KDD '00.

[6]  Qiang Yang,et al.  Extracting Actionable Knowledge from Decision Trees , 2007, IEEE Transactions on Knowledge and Data Engineering.

[7]  Vincent Kanade,et al.  Clustering Algorithms , 2021, Wireless RF Energy Transfer in the Massive IoT Era.

[8]  Ron Kohavi,et al.  Wrappers for feature selection , 1997 .

[9]  Hiroshi Oyama,et al.  A technique for identifying three diagnostic findings using association analysis , 2006, Medical & Biological Engineering & Computing.

[10]  R. Davé.  FUZZY SHELL-CLUSTERING AND APPLICATIONS TO CIRCLE DETECTION IN DIGITAL IMAGES , 1990 .

[11]  Usama M. Fayyad,et al.  Knowledge Discovery in Databases: An Overview , 1997, ILP.

[12]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[13]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[14]  Anthony J. T. Lee,et al.  Mining spatial association rules in image databases , 2007, Inf. Sci..

[15]  Heikki Mannila,et al.  Principles of Data Mining , 2001, Undergraduate Topics in Computer Science.

[16]  Yen-Liang Chen,et al.  A data mining approach for retail knowledge discovery with consideration of the effect of shelf-space adjacency on sales , 2006, Decis. Support Syst..

[17]  Sebastián Ventura,et al.  Educational data mining: A survey from 1995 to 2005 , 2007, Expert Syst. Appl..

[18]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[19]  Salvatore J. Stolfo,et al.  The merge/purge problem for large databases , 1995, SIGMOD '95.

[20]  Dennis Shasha,et al.  AJAX: an extensible data cleaning tool , 2000, SIGMOD '00.

[21]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[22]  Huan Liu,et al.  Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution , 2003, ICML.

[23]  Qiang Yang,et al.  Extracting Actionable Knowledge from Decision Trees , 2007 .

[24]  Isak Gath,et al.  Unsupervised Optimal Fuzzy Clustering , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  Tefko Saracevic,et al.  Information science: What is it? , 1968 .

[26]  Miin-Shen Yang,et al.  On cluster-wise fuzzy regression analysis , 1997, IEEE Trans. Syst. Man Cybern. Part B.

[27]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[28]  Pedro Larrañaga,et al.  An empirical comparison of four initialization methods for the K-Means algorithm , 1999, Pattern Recognit. Lett..