{"title":"Data Mining Applications in Entrepreneurship Analysis","authors":"Esther Hochsztain, A. Tasistro, M. Messina","doi":"10.1109/DMIA.2015.21","DOIUrl":null,"url":null,"abstract":"Creative entrepreneurship is considered an important factor in economic development achievement, specially in the knowledge-based society. Universities play a fundamental role in the process of entrepreneurial development and the entrepreneurship ecosystem.CCEEmprende is a program to support entrepreneurs developed by Facultad de Ciencias Económicas y de Administración - Universidad de la República, Uruguay. In this paper we present the use of data mining to improve decision making in entrepreneurship management, based on CCEEmprende projects data. A case study using several data mining and statistical techniques (association rules, decision trees, logistic regression) is developed with two goals: anticipating project success and identifying the most important factors related to project success/failure.","PeriodicalId":387758,"journal":{"name":"2015 International Workshop on Data Mining with Industrial Applications (DMIA)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Workshop on Data Mining with Industrial Applications (DMIA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DMIA.2015.21","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Creative entrepreneurship is considered an important factor in economic development achievement, specially in the knowledge-based society. Universities play a fundamental role in the process of entrepreneurial development and the entrepreneurship ecosystem.CCEEmprende is a program to support entrepreneurs developed by Facultad de Ciencias Económicas y de Administración - Universidad de la República, Uruguay. In this paper we present the use of data mining to improve decision making in entrepreneurship management, based on CCEEmprende projects data. A case study using several data mining and statistical techniques (association rules, decision trees, logistic regression) is developed with two goals: anticipating project success and identifying the most important factors related to project success/failure.
创造性创业被认为是经济发展成就的重要因素,特别是在知识型社会。大学在创业发展过程和创业生态系统中起着基础性作用。CCEEmprende是由乌拉圭科学学院Económicas y de Administración - República大学开发的一个支持企业家的项目。本文以CCEEmprende项目数据为基础,介绍了数据挖掘在创业管理决策中的应用。使用几种数据挖掘和统计技术(关联规则、决策树、逻辑回归)的案例研究有两个目标:预测项目成功和确定与项目成功/失败相关的最重要因素。