Evolving effective strategies for enhancing SHG's using K-mean clustering

B. Sajeev, K. Thangavel
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引用次数: 1

Abstract

Fast retrieval of relevant information from the databases has always been a significant issue. Different techniques have been developed for this purpose; one of them is Data mining. Clustering analysis is a key and easy tool in data mining and pattern recognition. In this paper K-Mean clustering is used for evaluating the performance of socially and economically backward group of people, self help groups (SHG's) 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 through structured questionnaire. The Parameters considered 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 and district wise analysis.
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基于k -均值聚类的SHG增强策略演化
从数据库中快速检索相关信息一直是一个重要的问题。为此目的开发了不同的技术;其中之一就是数据挖掘。聚类分析是数据挖掘和模式识别中一个关键而简单的工具。本文采用k均值聚类方法对喀拉拉邦社会经济落后群体——自助群体(self - help groups, SHG’s)的绩效进行了评价,并提出了改善社会经济状况的建议。通过结构化问卷从喀拉拉邦的9个地区收集了关于SHG成员的必要信息。该研究考虑的参数包括财务状况、获得的贷款类型、加入集团前后的资产改善情况、加入多个集团的影响以及地区明智分析。
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