基于数据采掘技术的非正式组织识别
Detecting Informal Organization Through Data Mining Techniques
摘要：
人力资源管理的主要课题之一是组织中的非正式组织，认识和管理这些非正式组织在组织中发挥着重要作用。一些管理者正试图认识到非正式组织之间的关系，并成为其中的一员，从而帮助正式组织的发展。识别非正式组织的方法很复杂，有时甚至是不可能的。本研究旨在提供一种利用数据采掘技术识别此类组织的方法。本研究将影响非正式组织创建的人力资源指标进行分类，包括组织员工的个人、社会和工作特征。然后，设计了一份调查问卷，并分发给员工。根据获得的数据创建数据库。本研究所应用的数据采掘技术为因子分析、K-均值聚类、决策树分类、GRI算法挖掘关联规则。最后，提出了一个模型，该模型适用于识别人与人之间的相似特征，以实现对非正式组织的最佳识别和对这些信息的利用。
英文摘要：
One of the main topics in human resources management is the subject of informal organizations in the organization such that recognizing and managing such informal organizations play an important role in the organizations. Some managers are trying to recognize the relations between informal organizations and being a member of them by which they could assist the formal organization development. Methods of recognizing informal organizations are complicated and occasionally even impossible. This study aims to provide a method for recognizing such organizations using data mining techniques. This study classifies indices of human resources influencing the creation of informal organizations, including individual, social, and work characteristics of an organizations employees. Then, a questionnaire was designed and distributed among employees. A database was created from obtained data. Applied data mining techniques in this study are factor analysis, clustering by K-means, classification by decision trees, and finally association rule mining by GRI algorithm. At the end, a model is presented that is applicable for recognizing the similar characteristics between people for optimal recognition of informal organizations and usage of this information.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
