Data Mining Applications in Human Resources Management System

Transcription

1 Data Mining Applications in Human Resources Management System Hubei university of automotive technology, China, Abstract This article applies data mining technology such as intelligent and automatic to the Human Resources Management System for the analysis of the situation making effective on a large amount of data information by using modem human resources management theory as a guide. And based on these fundamentals, we discover valuable knowledge to guide our actual work to enhance the personal ability of forest law administration. This article describes the resources and development and present condition at home and abroad on the topic of human resources management, analyzes the characteristics and present problems of human resource management and introduces the data mining technology. It introduces reference knowledge of data mining management, which makes systemic comparison and analysis on its process, methods, models and software, does pretreatment massive data of forestry law-executor management system by data mining technology, digs out potential relationship of the forestry law-executors in Inner Mongolia autonomous region and JiangSu province in this system, analyzes potential problems, all of which give support to management department. Keywords: data mining, intelligent, automatic, human resources management, potential relationship 1. Introduction Human resource management is raised to a higher level in the knowledge economic age, and many technologies have become one important part of human resource management. Even so, some problems are also left over. An advance technology would be found inevitably. Data mining is good at finding mode from data, which has been applied in many fields and obtained good economic results [1-3]. Data mining opens up new ideas for the solution to the problem described in this article. The research of data mining in the international has an earlier start, and the data mining research contents are related to several aspects. The data warehouse (DW) was proposed, which solved the early stage of data preparation for data mining [4, 5]. There were four main support data mining techniques [6]. There was a comprehensive introduction to data mining-related content [7]. In the data mining system development, the current in the world, there is more influential: SAS's Enterprise Miner, IBM's Intelligent Miner, Sybase's Warehouse Studio, and there are DB Miner, Quest, etc [8-12]. In China, data mining focused mainly on data mining correlation algorithm for the introduction and correction, and data mining in specific industry sectors the implementation of the architecture. Domestic data mining research area of general computer professional research, specifically for CRM data mining research is not enough. With the rapid development of information technology, in particular, the database technology and computer network are widely used, the amount of data of the forestry law enforcement agencies in the database are increased rapidly. Contain large amounts of data and information, the level of general ability of law enforcement officers, which will directly determine the level of ability on the implementation of forestry law enforcement system, and position in society and the masses. If this mass of data and information are done quickly and efficiently in-depth analysis and processing, you can find out the rules and patterns, to obtain the required knowledge for the forestry law enforcement agencies in personnel assignments, promotion of high-quality personnel to provide the correct basis for decision making [13, 14]. 2. Basis of data mining technology In this paper, data mining in forestry law enforcement agencies is the application of human resource management, and data mining is mainly used to solve problems in the management of human resources. The human resources management department of management functions is the customer, so the Journal of Convergence Information Technology(JCIT) Volume7, Number8, May 2012 doi: /jcit.vol7.issue

2 ultimate goal of human resource management is to make customers happy. Different customer needs are different, and there good clients and bad customers. Therefore, in addition to efforts to create customer satisfaction, the necessary customer management is very important. Data mining is usually in the areas of marketing, market outlook forecast, banking, insurance industry forecast with its long-term customer base. The introduction of human resource management here the concept of customer is the customer through data mining to classify, analyze how to maintain relationships with valuable customers, implement effective human resource management, improve forest law enforcement management of human resources management effectiveness. With the enhanced level of information, forest law enforcement agencies now have more complete information management systems, and human resources management has full based data, which can do data selection, pre-processing and conversion to do data preparation for data mining. The information at home and abroad shows that the cluster analysis methods and association rules of data mining in human resources management have been applied successfully. Based on the above analysis, human resource management of forestry law enforcement agencies meets the conditions for data mining applications, data mining research can explore the human resource management issues. In response to the national advocacy leading cadres of highly educated and younger, this paper hopes to use data mining of scientific knowledge on forest law enforcement personnel qualifications, the relationship between age and post excavation analysis, for the selection of highly educated and younger leading cadres to provide a scientific basis for decision making. 3. Implementation and technology base We use the traditional process of data mining framework for forest law enforcement officers that the first data collection storage work, and then choose some of the characteristics suitable for data mining properties, and choose the data clean-up, denoising preprocessing. After which we use the data mining analysis and clustering of association rules mining algorithm analysis to find out some internal rules and characteristics for subsequent decision-making and management services. Association rules analysis and clustering algorithm are the classic methods in the field of data mining, and the below is a brief introduction. 1. Association analysis. Association analysis is found in the data from a large number of interesting associations between item sets, correlation or causal structure, and item sets frequent patterns. Data exists in the database associated with a class of important knowledge that can be found. If two or more variables exist between the values of some regularity, it is called association. Association can be divided into simple association, temporal association and causal association. The purpose of association analysis is to identify hidden relationships in the database network. Sometimes the correlation function is not known in the database, even if they know is uncertain, so the rules are generated by association analysis with confidence. Correlation analysis finds the association rules, which shows attribute-value of the frequently together in a given data set conditions for the emergence. Association analysis is widely used in data analysis or shopping basket. The association rule is of the form X Y, that is,'' A 1 ^ A 2 ^... ^ Am B 1 ^ B 2 ^... ^ B n "rule, which, A i ( i {1,..., m)), B j ( j {1,..., n}) is the attribute - value pairs. The association rules X Y is interpreted as "to meet the conditions of X, most of the database tuples in Y also satisfy the conditions". For example, given AllElectronics relational database, a data mining system may be found in the form of association rules Age(X,"20 29")^income(X,"20k 29k") buys(x,"cd_player"). [support=2%,confidence=60%]. Where X is a variable that represents the customer. The rule is that the research AllElectronies 2% of customers (support) is between 20 and 29 years, and the annual income of 20k a 29k, and in AllElectronies buy CD player. The age and income group of customers likely to buy CD player is 60% (confidence or credibility). A marketing manager is assumed in Electronics, and you want to know in a transaction in which goods are often bought together. An example of this rule is: Contains(T,"computer") contains(t, "software"). 263

3 [support=1%,confidence=50%). The rule is that the transaction T contains "computer'', it also includes "software" of the possibility of 50% and l% of all transactions containing both ones. This rule involves a single attribute or predicates repeat (That is Contains), which contains a single predicate association rule called onedimensional association rules. Remove the predicate symbols, and the above rules can be written simply as computer => Software [1%, 50%] [15]. 2. Cluster analysis By unsupervised learning, according to the principle of the maximum similarity and minimizing similarity in the classes, data is automatically classified. Cluster analysis is to decompose a data set or divided into groups, so that the same group of points are similar to each other, but other groups are different in point[16]. Clustering algorithms can be roughly divided into the following categories: classification method, hierarchical method, density-based method, grid-based method and model-based method. 4. Data processing 4.1. Data Collection We collect the national level, provincial (autonomous regions and municipalities) level, municipal, county, four forest public security organs in the police station and primary forest 12 million civilian police-related information, sorting out the two forms, namely, personnel forms, and regional the table. Details are as follows: Table 1. List of people Serial number Variable name Type Length 1 Number Digital Integer 2 Name Text 30 3 ID number Text 18 4 Date of birth Date / Time 5 To work date Date / Time 6 Work unit Text Department Text Level Text 20 9 Post Digital Integer 10 Post Digital Integer 11 Resume Text Political landscape Digital Integer 13 Rank Digital Integer 14 Law enforcement category Text Education Digital Integer 16 People Text Photo OLE objects 18 Marital status Text 4 19 Birthplace Text Area Code Text 6 21 Zip Code Text 6 22 Address Text Home address Text Phone Text Remarks Text Remarks 264

4 Table 2. List of region Serial number Change name Type Length 1 Area code Text 6 2 Region name Text 50 3 Zip Code Text 6 4 Regional level Text 10 Table 1 is the analysis table, taken directly from the forestry law enforcement databases, as forestry law enforcement system database is not comprehensive enough, not perfect, so directly from the database table is still very rough person, there are many redundant words segment, and the existence of redundant field of data mining is bound to bring a lot of inconvenience, work efficiency, accuracy will be reduced, or even make the mining results from the truth, cause serious harm. Therefore, to refine this table, excluding unrelated field. To improve mining speed, the author of a number of related fields staff table to quantify, for the mining process to facilitate the mining results more convincing. Such as academic field is 2, then the corresponding degree of junior high school. As the education code in this paper plays an important role in the education field listed here to refine the results. Table 3. List of education background Code Implication Primary school Junior high school High School Secondary College University Master Dr Data selection In determining data mining of the business object, you need to search all business objects relevant internal and external data to choose the appropriate data in data mining applications. If data mining is based on the data warehouse, then the choice of data will be relatively simple, because the data warehouse for data mining are already ready to be the basic data for data mining. Otherwise, it is necessary to choose from a variety of data sources for data mining. This implies the need to integrate and consolidate data into a single repository of data mining, and coordinating data from multiple data sources on the differences in the values of these differences in the coordination of data values, which is key to the quality of data mining solutions. Multiple data sources in the main differences appear in the data definitions and methods used. Some data value conflicts are easy to find, such as same customer with several different (different system is used) address. There are also very subtle, as a customer has different names, and the worst is to have a different customer key. In the data preparation stage, these issues must be resolved. During the data selection, according to the needs of data mining, analysis of what data is clearly more important in data mining. This article focuses on the impact of "job" variable factors, it is necessary to remove the selected variables from the staff table of the data mining meaningful variables. Combined with the human resource management theory and related literature, you can learn gender, ethnicity, education, length of service, age have a higher impact on the job, specifically through the following model for analysis. Table 4 is a variable schedule after finishing: Table 4. Finally variable list No. Variable name Variable category Variable description 1 Gender Categorical variables 0-man, 1- female 2 People Categorical variables 0- Chinese, 1 - Minorities 3 Education Order of variables 1-Primary school, 2-Junior high school, 3- High School, 4-Secondary, 5-College, 6- University, 7-Master, 8-Dr. 4 Length of service Quantitative variables 5 Age Quantitative variables All persons of age in Pretreatment of data 265

5 In the choice of data, the need for data pre-processing and data cleaning solves the missing data values, redundant, inconsistent data, inconsistent of the data definitions, outdated data and other issues. These data are some of the dirty data. In data preprocessing, and sometimes need to group the data in order to improve efficiency of data mining to reduce the complexity of the model. The raw data obtained in the latter part of the merger, the integration process will inevitably be some missing and duplicate data according to ID number and the non-repetitive of the number, which removes the duplicate data. Data on key variables is checked the existence of key variables in order to avoid the phenomenon of missing data. Data mining process is composed of data selection and preprocessing of data about the prepare for the core. In these steps, the cost of the time or energy is more than the sum of the other steps. In the data preparation and model building process may be repeated several times, because the modeling process may well be the discovery of new problems to solve these new problems they need to modify the data Data transformation The main purpose of data transformation eliminates the data dimension from the initial feature to find truly useful features to reduce the data mining features to be considered or the number of variables. Data is converted into an analysis model for data mining. The model must start from the data analysis, and the first choice is the model variables, and then the original data indicates the new value, and then the subset of the data or samples are selected from the model, and the final conversion and selected variables are used to make consistent model of the algorithm. In the last stage of data pre-processing process, ultra-wide have been removed other than age and length of service, where these two variables should be further refined for future excavation work to lay a foundation. The current year minus year of birth is the present age, and the current year minus the year is the length of service to work. The classification is recorded by text encoding, such as gender, ethnicity, political affiliation, region, position, title and other personnel with basic information to show the form encoding categorical variables. The first two officers of the provincial ownership are distinguished according to region coding. For the political outlook, education and other variables, the raw data is the use of a coded form. This work does good prepare for data mining. It is found by observing the age variable, age, mostly in the range 20 to 60 years old, by Clementinel0 the Filter module to process it to 10 as a unit, the data re-division of age, and between 20 to 29 years of data defined as 1, and between 30 to 39 years of data is defined 3, and between 40 to 49 years of the data is defined 4. Variables are found by observing the length of service, and the length of service is concentrated in the range of 0 to 40 years, the Filter modules by Clementinel0 can be treated to 10 units, and 0 to 9 years of data is defined as 1, and 10 to 19 years of the data is defined as between 20 to 29 years of data is defined as 3, and over 30 years of data is defined as Data Mining 1. Personnel data analysis of national forestry law enforcement system This thesis is based on personnel data, and the indicators of the national forestry law enforcement system are a reference standard due to the age of law enforcement officers, and the qualifications and duties of the correlation determine the post-excavation analysis of the results-oriented, which is given the duties with age and the duties and qualifications associated. National data on age distribution of the different positions is shown in Fig.1. The effective data of this analysis is data. The managers in the national forestry law enforcement agencies are mainly in the age group, and this age group is mainly grass-roots cadres of the forestry law enforcement officers.the age of the division level, the deputy departmental level and departmental-level cadres are mainly in the age group, and the forestry law enforcement officers in middle and senior backbone, are experienced veteran. Grass-roots staff are 266

6 concentrated in the and the age of 50 and 59 in this age group, this is no work experience and some of the older employees in the forestry law enforcement officers. Qualifications of the various positions in the national data distribution are shown in Fig.2. From here you can see the country of the managers is mainly concentrated in the university and college level, where the proportion of university and college do not differ much. Deputy Director and Division-level cadres are mainly concentrated in the master, university, college three degrees. Deputy departmental level and departmental level-level cadres are mainly concentrated in the degree of Master of the two, which the department level cadres in the University and Master of the proportion of similar proportion of deputy departmental level cadres in the university degree is a master's degree more than twice that.8 PhD positions, they are external consultants by the analysis, so there is no duty Figure 1. Cross tabulation view of duty and age in the whole country PhD and Masters Specialist qualifications Undergraduate Specialist Manager Competent Director Administrator General staff Figure 2. Cross tabulation view of duty and antecedent in the whole country 2. Excavation of the personnel data of the Inner Mongolia region (1) Cluster analysis Inner Mongolia, the data is directly extracted from the data after the national pretreatment, which has a total 4390 data. This paper aims to explore what factors law enforcement officers duties influential in the clustering of the time to get rid of the duties of this variable. The basis of the data is selected in Inner Mongolia in general education, ethnicity, gender, seniority, age of these five variables to two-step clustering, by analyzing the clustering results to show the positions of each class within the distribution to analyze the given level of the variables for the positions. It can be seen in Table 5 that the four main differences are that these two variables of age and seniority, age of first category is mainly concentrated in the 38-year old, and the length of service is concentrated in about 18 years; and the age of the second category is mainly concentrated in about 41 years old, the length of service concentrated in about 20 years old: the age of the third category is mainly concentrated in the 36-year old, and the length of service is concentrated in about 16 years; and 267

7 the fourth class is mainly concentrated in the 48-year old, and the length of service is concentrated in about 29 years. Table 5. Two Step clustering results list in Inner Mongolia autonomous region Cluster-1 Cluster-2 cluster-3 cluster-4 Personnel Age Length of service The distribution of Inner Mongolia and the national average distribution are compared and analyzed: the proportion of no level staff in Inner Mongolia is far less than the national. Inner Mongolia region low-level staff, mainly due to the more remote areas of Inner Mongolia, while the upload data through the network, many the region's conditions do not allow, and therefore have not completed all the data upload. Staff Member level and above at all levels of staff in Inner Mongolia should be higher than the national. Table 6. Duty and age relationship distribution list in Inner Mongolia 20-29old 30-39old 40-49old More than 50 old Departmental level Deputy department Division level Deputy level Section level Deputy section Clerks level Clerk class No It can be seen from Table 6 that high-level personnel in the vice division level and above age group is too large, while the lower-level law enforcement officers are lower age, in line with the realities of working with the cadres. (2) Association rules Use Clementinel0.1 to establish the association model of the qualifications and duties, and the association rules deal with the data object, which must be qualitative data in a variable and must make the necessary data conversion. Variables which have been selected, the length of service variable and age variables are quantitative data, which must be converted into qualitative data. Association rules analyze the data of the Inner Mongolia region to hope to find useful rules. Support is set to 0.5% confidence level of 70%, and the largest number of the priori condition is 3, and the result is selected variables: position, ethnicity, length of service, age, education and gender. Focus on the relationship between duties and other variables. Results from the Clementine software are run out to pick out meaningful rules and explanation below. 1) Length of service: The age of 75% of the deputy departmental level are over 50 years. The length of service of 90% of deputy department is for years. 90% of deputy departmental level are male. These three rules show that deputy departmental level positions are mostly men with age, seniority. 2) The national question: 72.4% of the duties of the division level are Han Chinese. 72.7% of the section-level positions are Han Chinese. 85.4% of Deputy section duties are Han Chinese. 67.7% of forestry law enforcement system in Inner Mongolia are Han Chinese, and Deputy section cadres are more than 72% of Han Chinese, and some even more than 90%, which is a matter of concern. 3) Gender Issues: 90% of Deputy departmental level are male. 93.3% of the duties of the deputy level are male. 77.3% of the section level are male. 89.1% of Deputy section duties are male. 90.5% of staff grade are male. Taking into account the entire forestry system in Inner Mongolia 82.5% are male, you can see the women in his position to render the two small, in the middle of the distribution.very significant difference, and social roles of women, division of labor related to forestry system. 268

8 The results of the analysis from the entire association forestry duties, gender and ethnicity are relatively large, worthy of attention. 3. Jiangsu personnel data mining Jiangsu data is extracted from the data of the national preprocess, and the amount of data is First, the cluster will divided into four classes, and Cluster 1 contains 375 data, and Cluster2 contains 211 data, and Cluster3 contains 216 data, and Cluster 4 contains 343 data. Ethnicity variable has no significant effect on the clustering results, and the importance of its impact is 0.83, so the variables should removed to re-cluster. Re-select education, gender, length of service, age, these four variables, and the results will be run by Clementinel0.1, and Jiangsu's staff are divided into four categories as shown in Fig.3. Fig.3 Duty distribution figure in the sorts of Jiangsu province The clustering results also show the importance of job variables affect each variable: age variable, length of service variables, academic variables and gender variables for the clustering results have important implications, and the importance level achieves an average of The clustering results can be found in Jiangsu the national factor has no effect on the positions, because this may be the law enforcement system in Jiangsu without Han reasons. 6. Knowledge assimilation The duties and qualifications of the staff of the Inner Mongolia region does not matter, but have a close relationship with ethnicity, gender, seniority and age. This may be due to the degree of the Inner Mongolia Forestry law enforcement officers are mostly concentrated in the universities and college, so the degree of clustering has a very small impact. The degrees of the Inner Mongolia Forestry law enforcement officers are more concentrated (focused on university and college), so the highly educated of this trend is reflected very small. In accordance with the national average, college or university has the proportion of the Deputy section 3916/2932 = 1.3, the Clerks level ratio is 6270/2863 = 2.1, while college and university in Inner Mongolia proportion is 2211/1812 = 1.2. This value is slightly less than the Deputy section but significantly less than the level of Senior Staff. This shows that compared with the National Deputy section and Clerks level, and the education level of the Inner Mongolia Forestry law enforcement officers is be significantly higher than the national average, so it can reflect the leadership of highly educated trend in Inner Mongolia. The degrees of level cadres in Jiangsu province are all for the University. The education level should be close to the national level, and national level cadres educated mainly are concentrated in the master, university, college, while the proportion of master's and post-secondary are similar. Level cadres in Jiangsu reflect the obvious getting younger and younger like the national standard. It can 269

9 clearly reflects that this trend compared to the leading cadres of the Jiangsu region the country is younger. The above analysis can be seen that to promote the leading cadres of highly educated, younger under the premise of the country in various regions of the implementation is not the same in the country. Which should be the main reason for economic problems in areas with better economic conditions, such as the highly educated in Jiangsu trend is very obvious; in economically disadvantaged areas, such as Inner Mongolia, this trend is more obvious. This can be seen, the treatment system of the current forestry law enforcement system remains to be improved, and otherwise it is impossible to attract talent in the economically developed areas. 7. Conclusion In this paper, data mining technology is applied to the human resources management, and managers from large amounts of data need not be discovered, hidden mode, the manager's decision-making of these patterns will provide important guidance. Through the excavation of the personnel classification model analysis to draw a support of pre-defined result set, the trend of the distribution of leading cadres has a highly educated and younger. We obtain useful association rules through correlation analysis of the current distribution of staff. The purpose of the paper is depth study of data mining techniques and how to apply to human resources management, to help managers to make decisions. 8. References [1] Volker M, "Processing Relational OLAP Queries with UB-frees and Multidimensional Hierarchical Clustering", Proceedings of the International Workshop on Design and Management of Data Warehouses, pp , [2] Packianathan C, Alberto M, "Human Resource Management in Olympic Sport Organizations". Journal of Sport Management, USA, [3] Claude P, "Cements of Yesterday and Today: Concrete of Tomorrow", Cement and Concrete Research, vol. 30, no. 9, pp , [4] Malhotra V M, Hemming R T, "Blended Cements in North America", Cement and Concrete Composites, vol. 17, no. 1, pp , [5] Liu F, Ross M, Wang S M, "Energy Efficiency of China S Cement Industry", Great Britain, vol. 20, no. 7, pp , [6] Cheng Zengping, Kuang Xiangling, "Research and Application of Rough Set-based Phone Sales Outlets Decision", IJACT: International Journal of Advancements in Computing Technology, vol. 4, no. 2, pp , [7] Jozef B. Lewoc, Antoni Izworski, Slawomir Skowronski, Antonina Kieleczawa, Peter Kopacek, "An Integrated Manufacturing and Management System for Manufacturing Enterprises", JCIS: Journal of Communications and Information Sciences, vol. 1, no. 1, pp. 1-10, [8] Zhong Q, Wu S L, Xu H B, "Research on Management Requirements and Reference Framework for Infomationization of Cement Industry", Business Management and Information, vol. 37, no. 9, pp ,2008. [9] Frank S C, Annie Y H, "The Concept of Document Warehousing for Multi-dimensional Modeling of Textual-based Business Intelligence", Decision Support Systems, vol. 42, no.2, pp , [10] Wang L, "Data Warehouse and Business Intelligence", Journal of Zhanjiang Normal College, vol. 4, no.14, pp , [11] H Wu, M Gordon, K Demaagd, W Fan, "Mining web navigations for intelligence", Decision Support Systems, vol. 41, no.3, pp , [12] Rouibah K, Ould S, "A Concept and Prototype for Linking Business Intelligence to Business Strategy", Journal of Strategic Information Systems, vol. 11, no.2, pp , [13] Simitsis S, Vassiliadis P, "A Method for the Mapping of Conceptual Designs to Logical Blueprints for ETL Processes", Decision Support Systems, vol. 45, no.1, pp , [14] Pardillo J, Mazon J N, "Designing OLAP Schemata for Data Warehouses from Conceptual Models with MDA", Decision Support Systems, vol. 3, no.1, pp ,

10 [15] Chen RX, Fu YG Chen W B, "Business Intelligence System Based on Pentaho", Computer Engineering and Design,, vol. 29, no. 9, pp , [16] Yang J F, Dong PM, "Design and Development of Business Intelligence System", Journal of Dalian Railway Institute, vol. 23, no.7, pp ,