On Certain Patterns of Self Help Group Data: Using Clustering Approach
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Databases are growing in size to a stage where traditional techniques for analysis and visualization of the data are breaking down. In this book, knowledge of cluster analysis is applied to a socially relevant problem in India - Self Help Group (SHG). SHG has emerged as one of the most important models of socioeconomic development endeavours, particularly those aimed at women's empowerment, livelihoods strengthening and poverty alleviation. Cluster analysis is used to evaluate the performance of SHG groups. K-Means and Fuzzy C-Means algorithms are used to evaluate the financial status, socioec...
Databases are growing in size to a stage where traditional techniques for analysis and visualization of the data are breaking down. In this book, knowledge of cluster analysis is applied to a socially relevant problem in India - Self Help Group (SHG). SHG has emerged as one of the most important models of socioeconomic development endeavours, particularly those aimed at women's empowerment, livelihoods strengthening and poverty alleviation. Cluster analysis is used to evaluate the performance of SHG groups. K-Means and Fuzzy C-Means algorithms are used to evaluate the financial status, socioeconomic, loan pattern and financial inclusion of SHG members. New algorithms namely Modified K-Means, Modified Fuzzy C-Means and Hybrid of K-Means and Fuzzy C-Means is used to evaluate various parameters of SHG data. These algorithms are also used to study the impact analysis of financial inclusion through SHG bank linkage. Cluster analysis is found to be very efficient, easy and reliable tool than statistical methods in data analysis.
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