数据挖掘分类技术在企业人才招聘中的应用研究
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摘要
数据挖掘技术是利用分析工具从有噪声的、模糊的、随机的大量数据中,提取出隐含在内部的、未知的,又有潜在作用的知识和信息的过程,建立关系模型,并对其作出预测。分类挖掘技术是数据挖掘的核心技术之一,而分类技术中的决策树方法又是其中的重点。他能够直接体现出数据的特点,具有很好的分类预测能力,能够方便的提取出决策规则。
目前存在多种决策树方法,可以对大规模数据集进行分类,其中以ID3算法最为经典。ID3算法是以信息增益为选择分裂属性的标准来建立决策树的,能够很好的解决离散型属性的问题。
企业人才招聘问题是企业人力资源的核心和重点之一,它对于提高企业人才质量、提高企业在市场经济中的竞争力具有非常重要的意义。由于目前毕业生过多,企业不可能对每位学生都进行面试,因此就存在怎么选取适合自己企业的毕业生的问题。本文介绍了数据挖掘技术,并对分类技术的概念、算法等进行了论述,分析了决策树分类算法,详细论述了分类算法在企业人才招聘中应用的整个过程。包括样本数据的采集、数据的前期准备、ID3算法研究、根节点的确定以及最终决策树的建立。最后通过决策树得出分类模型的一般规则,这些规则可以对企业的人才招聘起指导作用,选出最适合企业的那部分人才。
本文以青岛三硕公司的信息数据为基础,运用数据挖掘技术对其数据库中的人才部分的数据进行分析,作出预测,为人才招聘提供技术支持。
Data mining technology is the use of analysis tools from the noisy, fuzzy, and a large number of random data, the extracted implicit in the internal, the unknown, another potential role of knowledge and information processes,the establishment of the relational model, and its forecasts.Category Mining is one of the core technology of data mining and classification techniques in the decision tree method is one of the key. He was able to directly reflect the characteristics of the data, with very good predictive power of classification can be easily extracted decision rules.
Currently there are several decision tree method can classify large data sets, of which the most classic ID3 algorithm.ID3 algorithm is based on information gain criteria for the selection of splitting the property to build a decision tree, can be a good solution to the problem of discrete attributes.
Talent recruitment problem is the core of corporate human resources and one of its priorities for improving the quality of corporate human resources, improve enterprise competitiveness in a market economy has very important significance. As currently too many graduates, businesses can not conduct interviews for each student, and therefore there is how their own businesses graduates select the appropriate issue. This article describes the data mining techniques, and classification technology, the concept of algorithm discussed in this paper analyzed the decision tree classification algorithm, discusses in detail the classification algorithm applied in the enterprise personnel recruiting throughout the process. Including the sample data collection, data preparation, ID3 algorithms, root identification and, ultimately, the decision tree building.The final adoption of the decision tree classification model derived a general rule, these rules can be played on the corporate recruitment guide to select the most suitable talent that part of the business.
This paper is based on Qingdao Sanshuo company's information, using data mining technology to its database of talents in some of the data analysis, make predictions, in order to recruit qualified personnel to provide technical support.
目前存在多种决策树方法,可以对大规模数据集进行分类,其中以ID3算法最为经典。ID3算法是以信息增益为选择分裂属性的标准来建立决策树的,能够很好的解决离散型属性的问题。
企业人才招聘问题是企业人力资源的核心和重点之一,它对于提高企业人才质量、提高企业在市场经济中的竞争力具有非常重要的意义。由于目前毕业生过多,企业不可能对每位学生都进行面试,因此就存在怎么选取适合自己企业的毕业生的问题。本文介绍了数据挖掘技术,并对分类技术的概念、算法等进行了论述,分析了决策树分类算法,详细论述了分类算法在企业人才招聘中应用的整个过程。包括样本数据的采集、数据的前期准备、ID3算法研究、根节点的确定以及最终决策树的建立。最后通过决策树得出分类模型的一般规则,这些规则可以对企业的人才招聘起指导作用,选出最适合企业的那部分人才。
本文以青岛三硕公司的信息数据为基础,运用数据挖掘技术对其数据库中的人才部分的数据进行分析,作出预测,为人才招聘提供技术支持。
Data mining technology is the use of analysis tools from the noisy, fuzzy, and a large number of random data, the extracted implicit in the internal, the unknown, another potential role of knowledge and information processes,the establishment of the relational model, and its forecasts.Category Mining is one of the core technology of data mining and classification techniques in the decision tree method is one of the key. He was able to directly reflect the characteristics of the data, with very good predictive power of classification can be easily extracted decision rules.
Currently there are several decision tree method can classify large data sets, of which the most classic ID3 algorithm.ID3 algorithm is based on information gain criteria for the selection of splitting the property to build a decision tree, can be a good solution to the problem of discrete attributes.
Talent recruitment problem is the core of corporate human resources and one of its priorities for improving the quality of corporate human resources, improve enterprise competitiveness in a market economy has very important significance. As currently too many graduates, businesses can not conduct interviews for each student, and therefore there is how their own businesses graduates select the appropriate issue. This article describes the data mining techniques, and classification technology, the concept of algorithm discussed in this paper analyzed the decision tree classification algorithm, discusses in detail the classification algorithm applied in the enterprise personnel recruiting throughout the process. Including the sample data collection, data preparation, ID3 algorithms, root identification and, ultimately, the decision tree building.The final adoption of the decision tree classification model derived a general rule, these rules can be played on the corporate recruitment guide to select the most suitable talent that part of the business.
This paper is based on Qingdao Sanshuo company's information, using data mining technology to its database of talents in some of the data analysis, make predictions, in order to recruit qualified personnel to provide technical support.
引文
[1]陈文伟,黄金才,赵新显著数据挖掘技术[M].北京：工业大学出版社,2002.7页
[2]Beckers,A.M,&BsatM.Z.Adss classification mode for research in Human Resource Information Systems[J].Information Systems Management,2002,19(3),41-50
[3]杨丽华,戴齐,杨占华.文本分类技术研究[J).软件时空,2006,209-211
[4]Zijian Zheng,Geoffrey I,Webb Lazy Learning of Bayesian Rules[J].Machine Lrarning, 2000,41,53-87
[5]王志浩.数据挖掘在招生信息处理系统中的应用研究[D).山东：山东师范大学,2006
[6]朱伟忠.数据挖掘决策树分类技术及应用研究[D].广东.华南理工大学,2004
[7]Jay-Louise Weldon. Data mining and visualization.Database Programming And Design.2006,9(5)
[8]罗海蛟.数据挖掘中分类算法的研究及其应用(J).微机发展.2003,13(22)：48-50
[9]栾丽华,吉根林.决策树分类技术研究[J].计算机工程,2004,(9)：94-96.
[10]魏晓云.决策树分类方法研究[J].计算机系统应用,2007(9)：42-43.
[11]QU-LAN J R.Induction of Decision Tree[J].Machine Learning,1986,(1):81-106.
[12]V.Kumar.M.V.Joshi,E-H.tan,and M.Steinbach.Heigh Performance Data Mining.In High Permance Computer for Computational Science,Pages 111-125.Springer,2002.
[13]S.K.Murth,S.Kasif, and S.Salzberg.A Sistem for induction of oblique decision trees.J of Article Inteligence Resrarch,2:1-33,2005.
[14]T.Mitchill.Machine Learning.Mcgrall-Hill,Bosson,MA,2002.
[15]B.M.E.Moret.Decision Trees and Diagrams.Computer Surveys,14(4):593-623,2003.
[16]杨明.决策树学习算法ID3的研究.计算机工程.2002.
[17]Mehmed Kantardzic.DATA MINING Concepts,Models,Methods,and Algorithms.IEEE Press.2004.
[18]李斌,钟毅芳,肖人彬.基于人机集成的生产过程管理系统研究与开发.工业工程与管理,2004,3(5)：58-62
[19]余舟毅,陈宗基,周锐.基于遗传算法的动态资源调度问题研究.控制与决策,2004,19(11)：1309-1311
[20]中国人民大学统计学系数据挖掘中心.数据挖掘中的决策树技术及其应用[J].统计与信息论坛,2005,17(2)：5-10
[21]王晓国,黄韶坤,朱炜,李启炎等.应用C4.5算法构造客户分类决策树的方法(J)计算机工程,2003,29(14)
[22]R.E.Schafian. The Boothing Approach To Machine Learning:an Overview. In MSRA Workshop ON Nonlinear Estimation and Classfision,2002.
[23]包晓安,钟乐海.基于ID3算法的快速分类方法研究[J].现代电子技术.2004,27(7)：84-85
[24]王小平,曹立明著.遗传算法-理论、应用与软件实现.西安：西安交通大学出版社,2002
[25]刘勇,康立山,等.非数值并行算法.北京：科学出版社,1995
[26]18J.R.Quinlan.C4.5:Programs For MaehineLearning[M].MorganKaufmann, SanFraneiseo,2006.
[27]Baker K. Introduction to Sequencing and Scheduling, John Wiley & Sons, New York,1974
[28]邵峰晶,于忠清著.数据挖掘原理与算法[M].北京：水利水电出版社,2003： 72-77
[29](美)01iviaParrRud著,朱扬勇等译,数据挖掘实践[M]。北京：机械工业出版社,2005：79-87
[30]俞文彬,谢康林,张忠能.基于属性分类的数据挖掘方法[J].小型微型计系统,2000,(3)：1-7
[31]Feng Zhao,Xiaoou Tang. Preprocessing and post-processing for Skeleton-based fingerprint minutiae extraction[J].Pattern Recognition,2007(40):1270-1281
[32]刘明吉,王秀峰,黄亚楼.数据挖掘中的数据预处理[J].计算机科学,2000,27(4)：54-57
[33]沈睿芳,时希杰,吴育华.基于数据仓库的数据预处理过程模型[J].计算机与数字工程,2005,33(9)：73-74
[34]陈小颖.人力资源管理系统中数据挖掘技术的应用：[硕士学位论文].武汉：武汉理工大学,2006
[35]谢邦昌.数据挖掘Clementine应用实务[M].北京：机械工业出版社,2008
[36]营志刚,金旭.数据挖掘中数据预处理的研究与实现[J].计算机应用研究,2004,7：112-116
[37]刘莉,徐玉生,马志新.数据挖掘中数据预处理技术综述(J).甘肃科学学报,2003,15(1)：117-123
[2]Beckers,A.M,&BsatM.Z.Adss classification mode for research in Human Resource Information Systems[J].Information Systems Management,2002,19(3),41-50
[3]杨丽华,戴齐,杨占华.文本分类技术研究[J).软件时空,2006,209-211
[4]Zijian Zheng,Geoffrey I,Webb Lazy Learning of Bayesian Rules[J].Machine Lrarning, 2000,41,53-87
[5]王志浩.数据挖掘在招生信息处理系统中的应用研究[D).山东：山东师范大学,2006
[6]朱伟忠.数据挖掘决策树分类技术及应用研究[D].广东.华南理工大学,2004
[7]Jay-Louise Weldon. Data mining and visualization.Database Programming And Design.2006,9(5)
[8]罗海蛟.数据挖掘中分类算法的研究及其应用(J).微机发展.2003,13(22)：48-50
[9]栾丽华,吉根林.决策树分类技术研究[J].计算机工程,2004,(9)：94-96.
[10]魏晓云.决策树分类方法研究[J].计算机系统应用,2007(9)：42-43.
[11]QU-LAN J R.Induction of Decision Tree[J].Machine Learning,1986,(1):81-106.
[12]V.Kumar.M.V.Joshi,E-H.tan,and M.Steinbach.Heigh Performance Data Mining.In High Permance Computer for Computational Science,Pages 111-125.Springer,2002.
[13]S.K.Murth,S.Kasif, and S.Salzberg.A Sistem for induction of oblique decision trees.J of Article Inteligence Resrarch,2:1-33,2005.
[14]T.Mitchill.Machine Learning.Mcgrall-Hill,Bosson,MA,2002.
[15]B.M.E.Moret.Decision Trees and Diagrams.Computer Surveys,14(4):593-623,2003.
[16]杨明.决策树学习算法ID3的研究.计算机工程.2002.
[17]Mehmed Kantardzic.DATA MINING Concepts,Models,Methods,and Algorithms.IEEE Press.2004.
[18]李斌,钟毅芳,肖人彬.基于人机集成的生产过程管理系统研究与开发.工业工程与管理,2004,3(5)：58-62
[19]余舟毅,陈宗基,周锐.基于遗传算法的动态资源调度问题研究.控制与决策,2004,19(11)：1309-1311
[20]中国人民大学统计学系数据挖掘中心.数据挖掘中的决策树技术及其应用[J].统计与信息论坛,2005,17(2)：5-10
[21]王晓国,黄韶坤,朱炜,李启炎等.应用C4.5算法构造客户分类决策树的方法(J)计算机工程,2003,29(14)
[22]R.E.Schafian. The Boothing Approach To Machine Learning:an Overview. In MSRA Workshop ON Nonlinear Estimation and Classfision,2002.
[23]包晓安,钟乐海.基于ID3算法的快速分类方法研究[J].现代电子技术.2004,27(7)：84-85
[24]王小平,曹立明著.遗传算法-理论、应用与软件实现.西安：西安交通大学出版社,2002
[25]刘勇,康立山,等.非数值并行算法.北京：科学出版社,1995
[26]18J.R.Quinlan.C4.5:Programs For MaehineLearning[M].MorganKaufmann, SanFraneiseo,2006.
[27]Baker K. Introduction to Sequencing and Scheduling, John Wiley & Sons, New York,1974
[28]邵峰晶,于忠清著.数据挖掘原理与算法[M].北京：水利水电出版社,2003： 72-77
[29](美)01iviaParrRud著,朱扬勇等译,数据挖掘实践[M]。北京：机械工业出版社,2005：79-87
[30]俞文彬,谢康林,张忠能.基于属性分类的数据挖掘方法[J].小型微型计系统,2000,(3)：1-7
[31]Feng Zhao,Xiaoou Tang. Preprocessing and post-processing for Skeleton-based fingerprint minutiae extraction[J].Pattern Recognition,2007(40):1270-1281
[32]刘明吉,王秀峰,黄亚楼.数据挖掘中的数据预处理[J].计算机科学,2000,27(4)：54-57
[33]沈睿芳,时希杰,吴育华.基于数据仓库的数据预处理过程模型[J].计算机与数字工程,2005,33(9)：73-74
[34]陈小颖.人力资源管理系统中数据挖掘技术的应用：[硕士学位论文].武汉：武汉理工大学,2006
[35]谢邦昌.数据挖掘Clementine应用实务[M].北京：机械工业出版社,2008
[36]营志刚,金旭.数据挖掘中数据预处理的研究与实现[J].计算机应用研究,2004,7：112-116
[37]刘莉,徐玉生,马志新.数据挖掘中数据预处理技术综述(J).甘肃科学学报,2003,15(1)：117-123
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