Building Data Mining Decision Tree Model for Predicting Employee Performance

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.

The role of decision trees in software development effort estimation (SDEE) has received increased attention across several disciplines in recent years thanks to their power of predicting, their ease of use, and understanding. Furthermore, there are a large number of published studies that investigated the use of a decision tree (DT) techniques in SDEE. Nevertheless, in reviewing the literature, a systematic literature review (SLR) that assesses the evidence stated on DT techniques is still lacking. The main issues addressed in this paper have been divided into five parts: prediction accuracy, performance comparison, suitable conditions of prediction, the effect of the methods employed in association with DT techniques, and DT tools. To carry out this SLR, we performed an automatic search over five digital libraries for studies published between 1985 and 2019. In general, the results of this SLR revealed that most DT methods outperform many techniques and show an improvement in accuracy when combined with association rules (AR), fuzzy logic (FL), and bagging. Additionally, it has been observed a limited use of DT tools: it is therefore suggested for researchers to develop more DT tools to promote the industrial utilization of DT amongst professionals.

Data mining refers to the application of data analysis techniques with the aim of extracting hidden knowledge from data by performing the tasks of pattern recognition and predictive modeling. This article describes the application of data mining techniques on educational data of a higher education institution in Croatia. Data used for the analysis are event logs downloaded from an e-learning environment of a real e-course. Data mining techniques applied for the research are cluster analysis and decision tree. The cluster analysis was performed by organizing collections of patterns into groups based on student behavior similarity in using course materials. Decision tree was the method of interest for generating a representation of decision-making that allowed defining classes of objects for the purpose of deeper analysis about how students learned.

This study aimed to identify patients with the highest risk of periodontal disease (PD), and to provide recommendations for the effective use and application of data mining (DM) techniques when establishing evidence-based dental-care policies for vulnerable groups at a high risk of PD. This study used the SEMMA (Sample, Explore, Modify, Model, and Assess) methodology to construct DM models based on data acquired from the fifth and sixth Korea National Health and Nutrition Examination Surveys (2000-2015). We analyzed the sociodemographic and comorbidity variables that influence PD by applying the popular DM techniques of decision-tree, neural-network, and regression models, and also attempted to improve the predictive power and reliability by comparing the results obtained by these three models. Our comparisons of the three DM algorithms confirmed that the average squared error, misclassification rate, receiver operating characteristic index, Gini coefficient, and Kolmogorov-Smirnov test results were the most appropriate for the decision-tree model. The analysis of the decision-tree model revealed that age and smoking status exert major effects on the risk of PD, and that stress and education level exert effects in rural areas, whereas education level, sex, hyperlipidemia, and alcohol intake exert effects in urban areas. We demonstrated that the decision-tree model is an effective DM technique for identifying the complex risk factors for PD. These results are expected to be helpful in improving the equality and efficacy of dental-care policies for vulnerable groups at a high risk of PD.

The purpose of this study was to develop a prediction model for the characteristics of older adults with depression using the decision tree method. A large dataset from the 2008 Korean Elderly Survey was used and data of 14,970 elderly people were analyzed. Target variable was depression and 53 input variables were general characteristics, family & social relationship, economic status, health status, health behavior, functional status, leisure & social activity, quality of life, and living environment. Data were analyzed by decision tree analysis, a data mining technique using SPSS Window 19.0 and Clementine 12.0 programs. The decision trees were classified into five different rules to define the characteristics of older adults with depression. Classification & Regression Tree (C&RT) showed the best prediction with an accuracy of 80.81% among data mining models. Factors in the rules were life satisfaction, nutritional status, daily activity difficulty due to pain, functional limitation for basic or instrumental daily activities, number of chronic diseases and daily activity difficulty due to disease. The different rules classified by the decision tree model in this study should contribute as baseline data for discovering informative knowledge and developing interventions tailored to these individual characteristics.

In general, obesity is calculated using the BMI, which is found to have limitations of use when we insert only one type of non-quantitative data as a crisp value, which is commonly used, or even create an obesity forecasting model using data mining techniques. If there is an input of weight and height data as a forecasting factor in various types of data, such as crisp value, estimated value and Fuzzy Linguistic Term value, etc. The algorithm will not possibly work under the data mining technique with such input data. This research presented the solution for this problem in order to enable data mining techniques under limitations of inserted data, including crisp value, estimated value and fuzzy linguistic term value, for forecasting obesity. The researchers created a fuzzy database system in the form of Conceptual Meta Schema to support fuzzy attributed data storage. In addition, there were 3 types of fuzzy attribute matching as follows; Type 1 was matching crisp value ​​with fuzzy linguistic term value, Type 2 was matching estimated value with fuzzy linguistic term value, and Type 3 was matching fuzzy linguistic term value with fuzzy linguistic term value, respectively. The attribute node from these fuzzy attributes would be selected to be used in the work of data mining techniques. Therefore, in this research, the researchers presented and compared the fuzzy model of working under the algorithm of data mining techniques in the form called fuzzy neural network and fuzzy decision tree techniques. The research result, it was found that the appropriate models for predicting obesity were fuzzy neural network algorithm with the neural network structure being 31-3-3, momentum at 0.2, learning rate at 0.3, under dividing data to test with cross-validation folds = 10 yielded accuracy value, precision value, recall value and f-measure at 84.3%, 82.7%, 84.3%, and 82.8%, respectively. Keywords: Obesity, Fuzzy Neural Net, Fuzzy Decision Tree, Fuzzy Attribute Matching, Forecasting Model

Decision trees are considered as one of the efficient classification techniques in data mining fields. But the standard decision trees are unfit to cope with data pervaded with uncertainty both at the construction and classification phase. Dempster-Shafer theory offers an alternative to traditional probabilistic theory for the mathematical representation of uncertainty. This paper added new aggregation combination operator and uncertainty measure operator into general framework for data mining based on evidence theory. Combining these two operators with decision tree and multidimensional cube, decision tree technique can be extended to uncertain environment. In the phase of node splitting, this algorithm can pre-prune the decision tree and generate a decision tree with fewer branches. Simulations have shown the effectiveness of this method.

Heart failure is a complex condition with a significant impact on patients' lives. A few studies have identified risk factors associated with rehospitalization among telehomecare patients with heart failure using logistic regression or survival analysis models. To date, there are no published studies that have used data mining techniques to detect associations with rehospitalizations among telehomecare patients with heart failure. This study is a secondary analysis of the home healthcare electronic medical record called the Outcome and Assessment Information Set-C for 552 telemonitored heart failure patients. Bivariate analyses using SAS and a decision tree technique using Waikato Environment for Knowledge Analysis were used. From the decision tree technique, the presence of skin issues was identified as the top predictor of rehospitalization that could be identified during the start of care assessment, followed by patient's living situation, patient's overall health status, severe pain experiences, frequency of activity-limiting pain, and total number of anticipated therapy visits combined. Examining risk factors for rehospitalization from the Outcome and Assessment Information Set-C database using a decision tree approach among a cohort of telehomecare patients provided a broad understanding of the characteristics of patients who are appropriate for the use of telehomecare or who need additional supports.

The apparel industry houses a huge amount and variety of data. At every step of the supply chain, data is collected and stored by each supply chain actor. This data, when used intelligently, can help with solving a good deal of problems for the industry. In this regard, this article is devoted to the application of data mining on the industry’s product data, i.e., data related to a garment, such as fabric, trim, print, shape, and form. The purpose of this article is to use data mining and symmetry-based learning techniques on product data to create a classification model that consists of two subsystems: (1) for predicting the garment category and (2) for predicting the garment sub-category. Classification techniques, such as Decision Trees, Naïve Bayes, Random Forest, and Bayesian Forest were applied to the ‘Deep Fashion’ open-source database. The data contain three garment categories, 50 garment sub-categories, and 1000 garment attributes. The two subsystems were first trained individually and then integrated using soft classification. It was observed that the performance of the random forest classifier was comparatively better, with an accuracy of 86%, 73%, 82%, and 90%, respectively, for the garment category, and sub-categories of upper body garment, lower body garment, and whole-body garment.

A loss‐of‐field (LOF) detection scheme is proposed based on the decision tree (DT) technique. The proposed method is a response‐based scheme. Instead of direct detection of LOF the proposed scheme provide the LOF protection based on simple measurements at generator terminal. The trajectories of generator terminal voltage and generated active and reactive powers are used as new features in LOF detection. The proposed DT‐based technique is robust and adaptive and keeps its performance under different fault scenarios. This scheme automatically adapts to favourable conditions near decision boundary and detects relevant features by the concept of information gain. The proposed method is simulated for a dynamic system under various fault conditions and the results are compared with a multilayer perceptron network classifier for various classification features.

This study aims to analyze the influence of work environment and work motivation on employee performance with job satisfaction as a mediating variable. A quantitative approach was used by a survey method of 158 employees of PT Cipta Panel Buana, a manufacturing company in Indonesia. Data was collected through a questionnaire based on the Likert scale and analyzed using Partial Least Square (PLS). The results of the study show that the work environment and work motivation have a significant effect on employee performance, both directly and indirectly through job satisfaction. Job satisfaction proved to be a powerful mediator in this relationship, with a 62.8% contribution to performance variability. These findings support the importance of creating a conducive work environment and an effective motivation system to improve employee satisfaction and performance. This research makes theoretical and practical contributions in the field of human resource management, especially in the context of the manufacturing industry

Since past few years data mining lot of attention related to knowledge like extracting methods in health care system like diabetes, cancer, CVS etc. There are lot of technique of data mining like decision tree, Naive base, KNN; J48 etc. are being used for prediction of diabetes. Diabetes is metabolic disorder related to poor absorption of insulin into body mussels or poor lowered secretion of insulin from pancreases. As this disease, this is main death causes disease in the world. So, prediction of these diseases with the help of data mining technique may help to protect many lives. In this study, we have to discuss various data mining technique, types of diabetes, application of these data mining technique. Prediction of diabetes or any other disease could play a significant role in health system. Data mining are very useful in the scenario. These techniques help in selection, understanding and designing of large size data to analysis the chances of diseases occurrence. Recently who has announced diseases a major cause of death worldwide. The prediction and identification early stage of diabetes can play major role to treat this disease significantly. Various data mining techniques like KNN, Decision tree, Naïve Bays etc. would be a significant asset for the researcher for gaining various data about diabetes, its causes, symptoms and possible treatment that have been using in the past and currently used by various physician. In this study we have briefly discussed various data mining techniques/models. Which have been currently used for diabetes prediction? Along with this discussion, we have also focused on performance and short coming of existing models/techniques time to time evaluated by researchers.

Employee performance is a key factor in achieving organizational success in today’s rapidly changing and competitive environment. Organizational culture, as a set of shared values, beliefs, and practices, plays a vital role in shaping employee behavior and outcomes. This study aims to investigate the impact of organizational culture on employee performance in public sector institutions in Soran, Kurdistan Region, and to determine which aspects of organizational culture contribute most to improving employee performance, providing practical suggestions for improving workplace outcomes. The research problem stems from the limited understanding of how specific cultural elements influence employee productivity, satisfaction, and efficiency. The main objective of this study is to identify which aspects of organizational culture most significantly contribute to improved employee performance, providing practical recommendations for enhancing workplace outcomes. To achieve this, primary data were collected using a structured questionnaire composed of 113 items, distributed randomly to public sector employees. A total of 293 valid responses were analyzed. Data were processed and statistically analyzed using the SPSS software for descriptive analysis, Chi-square tests, and Probit regression. Furthermore, the SPSS programming language was employed to construct and prune a Decision Tree model, which visualized key determinants of performance. The Decision Tree helped identify cultural support for skills development, job satisfaction, and efficient task execution as top predictors of high performance. Probit regression confirmed that organizational culture, education, marital status, and income significantly influence performance levels. The findings revealed a strong correlation between a supportive organizational culture and high employee performance. Employees who perceived their organizational culture as inclusive, communicative, and development-oriented reported greater task efficiency, stronger job commitment, and overall job satisfaction. The Decision Tree model achieved an accuracy rate of 85%, with an F1-score of 0.83, confirming the reliability of the model in identifying performance drivers. In conclusion, this study underscores the importance of aligning organizational culture with employee needs to drive productivity and engagement. It provides a strategic roadmap for public sector organizations to enhance performance through cultural transformation and targeted interventions.

This chapter is interested in discussing how to use data mining techniques to assist in achieving an acceptable level of quality of service of telecommunication systems. The quality of service is defined as the metrics which are predicated by using the data mining techniques, decision tree, association rules and neural networks. Routing algorithms can use this metric for optimal path selection which in turn will affect positively on the system performance. Also, in this chapter management axis using data mining techniques were handled, i.e., check the status of the telecommunication networks, role of data mining in obtaining optimal configuration, how to use data mining technique to assure high level of security for the telecommunication. The popularity of data mining in the telecommunications industry can be viewed as an extension of the use of expert systems in the telecommunications industry. These systems were developed to address the complexity associated with maintaining a huge network infrastructure and the need to maximize network reliability while minimizing labor costs (Liebowitz, J. 1988). The problem with these expert systems is that they are expensive to develop because it is both difficult and time consuming to elicit the requisite domain knowledge from experts.

There are various data mining techniques that are frequently used for the mining of vital patterns embedded within bulk data. These techniques include neural network, regression analysis, rough set theory, Bayesian network, decision trees, and so on. In this research, a novel data mining technique, genetically guided cluster based fuzzy decision tree (GCFDT), is introduced for the mining task. In order to test the efficacy of GCFDT, it is employed for building the predictive process models of reactive ion etching (RIE) with the aid of optical emission spectroscopy (OES) signals. This model endeavors to predict the wafer surface conditions for the new incoming set of process parameters. OES is an efficient tool for monitoring plasma emission intensity. In contrast with the C-fuzzy decision tree where granules are devolved through fuzzy clustering here, granulation is practised through genetically guided fuzzy clustering. The growth of the tree is governed by expanding the node having highest diversity. The results obtained by employing CGFDT in RIE process modeling reveal that it dominates both the traditional C-fuzzy decision trees and C4.5 decision trees in terms of both the accuracy and compactness.