• Management Science and Financial Engineering
• /
• v.18 no.2
• /
• pp.13-17
• /
• 2012

Set covering has widely been accepted as a staple tool for feature selection in data mining. We present a generalized version of this classical combinatorial optimization model to make it better suited for the purpose and propose a surrogate relaxation-based procedure for its meta-heuristic solution. Mathematically and also numerically with experiments on 25 set covering instances, we demonstrate the utility of the proposed model and the proposed solution method.

• Journal of the Korea Society of Computer and Information
• /
• v.11 no.6 s.44
• /
• pp.59-67
• /
• 2006

If apply BPM that is a business process management tool to SCM sector, efficient process management and control are available. Also, BPM can execute integrating process that compose SCM effectively. These access method does to manage progress process of SCM process more efficiently and do monitoring. Also, It is can be establish plan about improvement of process analyzing process achievement result. Thus, in this paper, introduce this BPM into SCM environment. Also, SCM process presents plan that executes integration and improves business process effectively applying data mining technique.

• International Journal of Aeronautical and Space Sciences
• /
• v.11 no.4
• /
• pp.247-265
• /
• 2010

Multi-objective design exploration (MODE) and its applications are reviewed as an attempt to utilize numerical simulation in aerospace engineering design. MODE reveals the structure of the design space based on trade-off information. A self-organizing map (SOM) is incorporated into MODE as a visual data mining tool for the design space. SOM divides the design space into clusters with specific design features. This article reviews existing visual data mining techniques applied to engineering problems. Then, we discuss three applications of MODE: multidisciplinary design optimization for a regional-jet wing, silent supersonic technology demonstrator and centrifugal diffusers.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.30 no.3
• /
• pp.62-70
• /
• 2007

This study define the concepts of port logistics service by investigating various elements of port logistics service and grouping them in six attributes using a Data Mining technique. The QFD (Quality Function Deployment) technique is applied to measure the quality of port logistics service, and those results are analyzed. The DMQFD (Quality Function Deployment using Data Mining) model proposed in this study is a model for analyzing of port logistics service quality which is produced by combining those two stages. Using the DMQFD model, the requirements of customer could understand more correctly and systematically, and it could be an alternative tool to accomplish a customer satisfaction.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2006.11a
• /
• pp.408-411
• /
• 2006

Currently, the nano economy threatens the mass economy. This is based on the internet business models. In the nano business models based on internet, the diversely personalized services are needed. Many researches of the personalization on the web have been studied. The web usage mining using click stream data is a tool for personalization model. In this paper, we propose an internet business model using evolutionary support vector machine and web response survey as a web usage mining. After analyzing click stream data for web usage mining, a personalized service model is constructed in our work. Also, using an approach of web response survey, we improve the performance of the customers' satisfaction. From the experimental results, we verify the performance of proposed model using two data sets from KDD Cup 2000 and our web server.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.16 no.8
• /
• pp.5591-5599
• /
• 2015

For the effective energy management, this study proposed BIM(Building Information Modeling)-based Data Mining Model(B-DMM). To conduct this, BIM-based data mining researches were surveyed then the use-cases and scenarios related to the energy management were analyzed. By using this results, B-DMM for supporting the decision making related the energy management was proposed. The output will be used as a decision making tool for managing a building energy.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.9
• /
• pp.2564-2588
• /
• 2024

Due to COVID19 pandemic, most of educational institutions and schools changed the traditional way of teaching to online teaching and learning using well-known Learning Management Systems (LMS) such as Moodle, Canvas, Blackboard, etc. Accordingly, LMS started to generate a large data related to students' characteristics and achievements and other course-related information. This makes it difficult to teachers to monitor students' behaviour and performance. Therefore, a need to support teachers with a tool alerting student who might be in risk based on their recorded activities and achievements in adopted LMS in the school. This paper focuses on the benefits of using recorded data in LMS platforms, specifically Moodle, to predict students' performance by analysing their behavioural data and engagement activities using data mining techniques. As part of the overall process, this study encountered the task of extracting and selecting relevant data features for predicting performance, along with designing the framework and choosing appropriate machine learning techniques. The collected data underwent pre-processing operations to remove random partitions, empty values, duplicates, and code the data. Different machine learning techniques, including k-NN, TREE, Ensembled Tree, SVM, and MLPNNs were applied to the processed data. The results showed that the MLPNNs technique outperformed other classification techniques, achieving a classification accuracy of 93%, while SVM and k-NN achieved 90% and 87% respectively. This indicates the possibility for future research to investigate incorporating other neural network methods for categorizing students using data from LMS.

• The Journal of Bigdata
• /
• v.1 no.2
• /
• pp.65-78
• /
• 2016

In the rapid change of business environment, it is crucial that several companies with core competence cooperate together in order to deliver competitive products to the market faster. Thus a lot of companies are participating in supply chains and SCM (Supply Chain Management) become more important. To efficiently manage supply chains, the analysis of data from SCM systems is required. In this paper, we explain how to analyze SCM related data with process mining techniques. After discussing the data requirement for process mining, several process mining techniques for the data analysis are explained. To show the applicability of the techniques, we have performed a case study with a company in South Korea. The case study shows that process mining is useful tool to analyze SCM data. On specifically, an overall process, several performance measures, and social networks can be easily discovered and analyzed with the techniques.

• Journal of the Korea Society of Computer and Information
• /
• v.21 no.8
• /
• pp.65-75
• /
• 2016

In this paper, we present a novel approach to help MSR researchers obtain necessary data with a tool, termed General Purpose Extractor for Source code (GPES). GPES has a single function extracts high-quality data, e.g., the version history, abstract syntax tree (AST), changed code diff, and software quality metrics. Moreover, features such as an AST of other languages or new software metrics can be extended easily given that GPES has a flexible data model and a component-based design. We conducted several case studies to evaluate the usefulness and effectiveness of our tool. Case studies show that researchers can reduce the overall cost of data analysis by transforming the data into the required formats.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.11 no.4
• /
• pp.33-45
• /
• 2015

Currently, Internet is used an essential tool in the business area. Despite this importance, there is a risk of network attacks attempting collection of fraudulence, private information, and cyber terrorism. Firewalls and IDS(Intrusion Detection System) are tools against those attacks. IDS is used to determine whether a network data is a network attack. IDS analyzes the network data using various techniques including expert system, data mining, and state transition analysis. This paper tries to compare the performance of two data mining models in detecting network attacks. They are decision tree (C4.5), and neural network (FANN model). I trained and tested these models with data and measured the effectiveness in terms of detection accuracy, detection rate, and false alarm rate. This paper tries to find out which model is effective in intrusion detection. In the analysis, I used KDD Cup 99 data which is a benchmark data in intrusion detection research. I used an open source Weka software for C4.5 model, and C++ code available for FANN model.