基于决策树的人才管理系统的设计与实现
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摘要
在企业管理实践中,人才管理已成为其重要的组成内容。人才是企业的第一资本。随着社会主义现代化建设的不断发展,科技的不断进步,市场竞争愈来愈激烈,企业对人才素质的要求也愈来愈高,市场经济的竞争最终体现在人才的角逐上。谁拥有一支高素质的人才队伍,谁就有了成功的基础。因此,加强人才管理是企业管理创新的核心。当前,企业对人才管理水平的要求越来越高,所产生的管理技术也越来越丰富。但是即使这样,在人才管理技术领域仍然存在很多现有技术无法解决的问题。这些问题包括:如何做到人才管理制度制定的科学化和可论证性?在人才管理中存在大量的定型数据,这些定性数据该如何科学的管理和比较分析?如何利用计算机技术从现有的大量人才信息数据冲挖掘出对企业人事决策有用的具体数学模型等等。所有这些问题都制约着人才管理的进一步发展,和人才管理系统软件的应用。
数据挖掘(Data Mining),就是从大量数据中获取有效的、新颖的、潜在有用的、最终可理解的模式的非平凡过程。数据挖掘的广义观点:数据挖掘就是从存放在数据库,数据仓库或其他信息库中的大量的数据中“挖掘”有趣知识的过程。分类在数据挖掘中是一项非常重要的任务,目前在商业上应用的最多。在数据挖掘分类技术中,主要有决策树法、贝叶斯法、神经网络法、遗传算法和粗糙集等算法。
本文以“基于决策树的人才管理系统的设计与实现”为题目,设计并实现了一个基于决策树的人才管理系统,本系统主要包括了人事管理主系统和数据挖掘子系统两大部分。其中人事管理主系统主要完成日常人事档案管理的相关业务操作,而数据挖掘子系统是根据数据挖掘中的决策树分类算法,对人才数据进行数据挖掘,为人才决策提供支持。并取得了良好的使用效果。
The talent management has become an important part of enterprise management. Talent is the first capital of enterprise. With the development of science and technology and socialist modernization construction, market competition becomes more and more severe and demand of enterprises' talent qualities becomes more and more demanding.
Competitions in the market economy lies in talent compete finally. The enterprise which has a high-caliber talent cadre will have the key to success. So it is the core of business management innovation for enterprise to strengthen talent management. Today enterprise is more attentions to talent management. From the simple statistical calculation to Econometrics and application of the computer system, the management technology becomes more and more. Even though, there are still many problems of talent management which can’t be solved effectively, such as how to make rules for talent management and proof its foundation, how to compare and analyze the qualitative data in talent management and how to build a model from massive talent data by data mining for talent management which can describe the quality of talent. These problems restrict the development of talent management and we look forward to find a new method for them.
Data Mining is a uncommon process of identifying effective new potentially useful and finally accessible patterns. Generalized Definition of data mining is a process which can find interesting knowledge from information library. The information library, such database, data warehouse and other information library, stored mass data. Classification is a important task of data mining and it is most applied in business. The classification technology of data mining mainly includes decision-tree method, baye’method, neural network method, genetic algorithm and Fuzzy-rough Algorithm.
This thesis’s title is the design and realize of talent management system based on decision-tree algorithm. A talent management system based on decision-tree algorithm is designed and realized. The talent management system includes personnel management main system and data mining subsystem. The personnel management main system executes the business operations of daily personnel management. The data mining subsystem based on decision-tree algorithm can mine the talent data and provide support to personnel decision-making. The system also has a sound effect.
数据挖掘(Data Mining),就是从大量数据中获取有效的、新颖的、潜在有用的、最终可理解的模式的非平凡过程。数据挖掘的广义观点:数据挖掘就是从存放在数据库,数据仓库或其他信息库中的大量的数据中“挖掘”有趣知识的过程。分类在数据挖掘中是一项非常重要的任务,目前在商业上应用的最多。在数据挖掘分类技术中,主要有决策树法、贝叶斯法、神经网络法、遗传算法和粗糙集等算法。
本文以“基于决策树的人才管理系统的设计与实现”为题目,设计并实现了一个基于决策树的人才管理系统,本系统主要包括了人事管理主系统和数据挖掘子系统两大部分。其中人事管理主系统主要完成日常人事档案管理的相关业务操作,而数据挖掘子系统是根据数据挖掘中的决策树分类算法,对人才数据进行数据挖掘,为人才决策提供支持。并取得了良好的使用效果。
The talent management has become an important part of enterprise management. Talent is the first capital of enterprise. With the development of science and technology and socialist modernization construction, market competition becomes more and more severe and demand of enterprises' talent qualities becomes more and more demanding.
Competitions in the market economy lies in talent compete finally. The enterprise which has a high-caliber talent cadre will have the key to success. So it is the core of business management innovation for enterprise to strengthen talent management. Today enterprise is more attentions to talent management. From the simple statistical calculation to Econometrics and application of the computer system, the management technology becomes more and more. Even though, there are still many problems of talent management which can’t be solved effectively, such as how to make rules for talent management and proof its foundation, how to compare and analyze the qualitative data in talent management and how to build a model from massive talent data by data mining for talent management which can describe the quality of talent. These problems restrict the development of talent management and we look forward to find a new method for them.
Data Mining is a uncommon process of identifying effective new potentially useful and finally accessible patterns. Generalized Definition of data mining is a process which can find interesting knowledge from information library. The information library, such database, data warehouse and other information library, stored mass data. Classification is a important task of data mining and it is most applied in business. The classification technology of data mining mainly includes decision-tree method, baye’method, neural network method, genetic algorithm and Fuzzy-rough Algorithm.
This thesis’s title is the design and realize of talent management system based on decision-tree algorithm. A talent management system based on decision-tree algorithm is designed and realized. The talent management system includes personnel management main system and data mining subsystem. The personnel management main system executes the business operations of daily personnel management. The data mining subsystem based on decision-tree algorithm can mine the talent data and provide support to personnel decision-making. The system also has a sound effect.
引文
[1]陆秋,程小辉.基于属性相似度的决策树算法[J].计算机工程, 2009, (06) .
[2]谢枫平.数据分类中决策树算法的一些改进[J].龙岩学院学报, 2009, (02) .
[3]洪雪飞,徐维祥.基于变精度粗糙集的决策树改进方法[J].计算机工程与应用, 2009, (13) .
[4]傅明,张颖,陈曦,杨峰.基于程序演化的决策树优化算法[J].计算机工程与设计, 2009, (09) .
[5]刘祺.决策树ID3算法的改进研究[D].哈尔滨工程大学, 2009 .
[6]曹丽英.决策树在耕地地力等级评价中的应用研究[D].长春理工大学, 2009 .
[7]滕皓,赵国毅,韩保胜.改进决策树的研究[J].济南大学学报(自然科学版), 2002,(03) .
[8]建立数据挖掘为基础的客户关系管理体系[J].统计与信息论坛, 2002,(04) .
[9]王清毅,张波,蔡庆生.目前数据挖掘算法的评价[J].小型微型计算机系统, 2000,(01) .
[10]俞文彬,谢康林,张忠能.基于属性分类的数据挖掘方法[J].小型微型计算机系统, 2000,(03) .
[11]宋莉芳.以数据挖掘为指导建立高校信息管理体系[J].成人高教学刊, 2004,(01) .
[12]张宜生,刘凡,梁书云.人力资源数据挖掘技术及其应用[J].计算机工程与应用, 2002,(06) .
[13]陈丽,陈根才.基于高校人事信息库的数据挖掘研究[J].计算机工程, 2000,(11) .
[14]杨明,孙志挥,宋余庆.快速更新全局频繁项目集[J].软件学报, 2004,(08) .
[15]陆介平,杨明,孙志挥,鞠时光.快速挖掘全局最大频繁项目集[J].软件学报, 2005,(04)
[16]姜彬,赵广荣.基于数据仓库基础上的数据挖掘技术综述[J].山东电大学报, 2004,(02)
[17]陈小波,蒋方纯.高等院校数据仓库系统的构建与实施[J].深圳信息职业技术学院学报, 2003,(01) .
[18]接励,王虹.高校人事管理信息中的关联规则挖掘[J].天津师范大学学报(自然科学版), 2004,(02) .
[19]李建忠.数据挖掘技术[J].中国金融电脑, 1997,(08) .
[20]潘定,沈钧毅.时态数据挖掘的相似性发现技术[J].软件学报, 2007,(02) .
[21] Robert E. Schapire. The Strength of Weak Learnability[J]. Machine Learning, 1990,5, (2) .
[22] Claudio Carpineto,Giovanni Romano. A lattice conceptual clustering system and its application to browsing retrieval[J] Machine Learning, 1996,24, (2) .
[23] Leo Breiman. Bagging Predictors[J] Machine Learning, 1996,24, (2) .
[24] Tjen-Sien Lim,Wei-Yin Loh,Yu-Shan Shih. A Comparison of Prediction Accuracy, Complexity,and Training Time of Thirty-Three Old and New Classification Algorithms[J] Machine Learning, 2000,40, (3) .
[25] Alexander Hinneburg,Daniel A. Keim. A General Approach to Clustering in Large Databases with Noise[J] Knowledge and Information Systems, 2003,5, (4) .
[26] Clark Glymour,David Madigan,Daryl Pregibon,Padhraic Smyth. Statistical Themes and Lessons for Data Mining[J] Data Mining and Knowledge Discovery, 1997,1, (1) .
[27] Craig Silverstein,Sergey Brin,Rajeev Motwani. Beyond Market Baskets: Generalizing Association Rules to Dependence Rules[J] Data Mining and Knowledge Discovery, 1998,2, (1)
[28] Jef Wijsen,Robert Meersman. On the Complexity of Mining Quantitative Association Rules[J] Data Mining and Knowledge Discovery, 1998,2, (3) .
[29] Jon Kleinberg,Christos Papadimitriou,Prabhakar Raghavan. A Microeconomic View of Data Mining[J] Data Mining and Knowledge Discovery, 1998,2, (4) .
[30] J. Ben Schafer,Joseph A. Konstan,John Riedl. E-Commerce Recommendation Applications[J] Data Mining and Knowledge Discovery, 2001,5, (1-2) .
[2]谢枫平.数据分类中决策树算法的一些改进[J].龙岩学院学报, 2009, (02) .
[3]洪雪飞,徐维祥.基于变精度粗糙集的决策树改进方法[J].计算机工程与应用, 2009, (13) .
[4]傅明,张颖,陈曦,杨峰.基于程序演化的决策树优化算法[J].计算机工程与设计, 2009, (09) .
[5]刘祺.决策树ID3算法的改进研究[D].哈尔滨工程大学, 2009 .
[6]曹丽英.决策树在耕地地力等级评价中的应用研究[D].长春理工大学, 2009 .
[7]滕皓,赵国毅,韩保胜.改进决策树的研究[J].济南大学学报(自然科学版), 2002,(03) .
[8]建立数据挖掘为基础的客户关系管理体系[J].统计与信息论坛, 2002,(04) .
[9]王清毅,张波,蔡庆生.目前数据挖掘算法的评价[J].小型微型计算机系统, 2000,(01) .
[10]俞文彬,谢康林,张忠能.基于属性分类的数据挖掘方法[J].小型微型计算机系统, 2000,(03) .
[11]宋莉芳.以数据挖掘为指导建立高校信息管理体系[J].成人高教学刊, 2004,(01) .
[12]张宜生,刘凡,梁书云.人力资源数据挖掘技术及其应用[J].计算机工程与应用, 2002,(06) .
[13]陈丽,陈根才.基于高校人事信息库的数据挖掘研究[J].计算机工程, 2000,(11) .
[14]杨明,孙志挥,宋余庆.快速更新全局频繁项目集[J].软件学报, 2004,(08) .
[15]陆介平,杨明,孙志挥,鞠时光.快速挖掘全局最大频繁项目集[J].软件学报, 2005,(04)
[16]姜彬,赵广荣.基于数据仓库基础上的数据挖掘技术综述[J].山东电大学报, 2004,(02)
[17]陈小波,蒋方纯.高等院校数据仓库系统的构建与实施[J].深圳信息职业技术学院学报, 2003,(01) .
[18]接励,王虹.高校人事管理信息中的关联规则挖掘[J].天津师范大学学报(自然科学版), 2004,(02) .
[19]李建忠.数据挖掘技术[J].中国金融电脑, 1997,(08) .
[20]潘定,沈钧毅.时态数据挖掘的相似性发现技术[J].软件学报, 2007,(02) .
[21] Robert E. Schapire. The Strength of Weak Learnability[J]. Machine Learning, 1990,5, (2) .
[22] Claudio Carpineto,Giovanni Romano. A lattice conceptual clustering system and its application to browsing retrieval[J] Machine Learning, 1996,24, (2) .
[23] Leo Breiman. Bagging Predictors[J] Machine Learning, 1996,24, (2) .
[24] Tjen-Sien Lim,Wei-Yin Loh,Yu-Shan Shih. A Comparison of Prediction Accuracy, Complexity,and Training Time of Thirty-Three Old and New Classification Algorithms[J] Machine Learning, 2000,40, (3) .
[25] Alexander Hinneburg,Daniel A. Keim. A General Approach to Clustering in Large Databases with Noise[J] Knowledge and Information Systems, 2003,5, (4) .
[26] Clark Glymour,David Madigan,Daryl Pregibon,Padhraic Smyth. Statistical Themes and Lessons for Data Mining[J] Data Mining and Knowledge Discovery, 1997,1, (1) .
[27] Craig Silverstein,Sergey Brin,Rajeev Motwani. Beyond Market Baskets: Generalizing Association Rules to Dependence Rules[J] Data Mining and Knowledge Discovery, 1998,2, (1)
[28] Jef Wijsen,Robert Meersman. On the Complexity of Mining Quantitative Association Rules[J] Data Mining and Knowledge Discovery, 1998,2, (3) .
[29] Jon Kleinberg,Christos Papadimitriou,Prabhakar Raghavan. A Microeconomic View of Data Mining[J] Data Mining and Knowledge Discovery, 1998,2, (4) .
[30] J. Ben Schafer,Joseph A. Konstan,John Riedl. E-Commerce Recommendation Applications[J] Data Mining and Knowledge Discovery, 2001,5, (1-2) .