• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.2029-2032
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• 2004

The quantity of data were rapidly increased recently and caused the data overwhelming. This led to be difficult in searching the required data. The method of eliminating redundant data was needed. One of the efficient methods was Knowledge Discovery in Database (KDD). Generally data can be separate into 2 cases, continuous data and discrete data. This paper describes algorithm that transforms continuous attributes into discrete ones. We present an Improved Class Attribute Interdependence Maximization (ICAIM), which designed to work with supervised data, for discretized process. The algorithm does not require user to predefine the number of intervals. ICAIM improved CAIM by using significant test to determine which interval should be merged to one interval. Our goal is to generate a minimal number of discrete intervals and improve accuracy for classified class. We used iris plant dataset (IRIS) to test this algorithm compare with CAIM algorithm.

• Journal of the Korea Society of Computer and Information
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• v.22 no.1
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• pp.23-32
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• 2017

We can obtain useful knowledge from data by using a subgroup discovery algorithm. Subgroup discovery is a rule model learning method that finds data subgroups containing specific information from data and expresses them in a rule form. Subgroups are meaningful as they account for a high percentage of total data and tend to differ significantly from the overall data. Subgroup is expressed with conjunction of only literals previously. So, the scope of the rules that can be derived from the learning process is limited. In this paper, we propose a method to increase expressiveness of rules through internal disjunctive representation of attribute values. Also, we analyze the characteristics of existing subgroup discovery algorithms and propose an improved algorithm that complements their defects and takes advantage of them. Experiments are conducted with the traffic accident data given from Busan metropolitan city. The results shows that performance of the proposed method is better than that of existing methods. Rule set learned by proposed method has interesting and general rules more.

• Journal of Korea Society of Industrial Information Systems
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• v.2 no.2
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• pp.189-207
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• 1997

Recently, we have witnessed a host of emerging tools in the management support systems (MSS) area including the data warehouse/multidimensinal databases (MDDB), data mining, on-line analytical processing (OLAP), intelligent agents, World Wide Web(WWW) technologies, the Internet, and corporate intranets. These tools are reshaping MSS developments in organizations. This article reviews a set of emerging data management technologies in the knowledge discovery in databases(KDD) process and analyzes their implications for decision support. Furthermore, today's MSS are equipped with a plethora of AI techniques (artifical neural networks, and genetic algorithms, etc) fuzzy sets, modeling by example , geographical information system(GIS), logic modeling, and visual interactive modeling (VIM) , All these developments suggest that we are shifting the corporate decision making paradigm form information-driven decision making in the1980s to knowledge-driven decision making in the 1990s.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.691-708
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• 2014

This paper formalizes a special type of social networking knowledge, which is called "workflow performer-role affiliation networking knowledge." A workflow model specifies execution sequences of the associated activities and their affiliated relationships with roles, performers, invoked-applications, and relevant data. In Particular, these affiliated relationships exhibit a stream of organizational work-sharing knowledge and utilize business process intelligence to explore resources allotting and planning knowledge concealed in the corresponding workflow model. In this paper, we particularly focus on the performer-role affiliation relationships and their implications as organizational and business process intelligence in workflow-driven organizations. We elaborate a series of theoretical formalisms and practical implementation for modeling, discovering, and visualizing workflow performer-role affiliation networking knowledge, and practical details as workflow performer-role affiliation knowledge representation, discovery, and visualization techniques. These theoretical concepts and practical algorithms are based upon information control net methodology for formally describing workflow models, and the affiliated knowledge eventually represents the various degrees of involvements and participations between a group of performers and a group of roles in a corresponding workflow model. Finally, we summarily describe the implications of the proposed affiliation networking knowledge as business process intelligence, and how worthwhile it is in discovering and visualizing the knowledge in workflow-driven organizations and enterprises that produce massively parallel interactions and large-scaled operational data collections through deploying and enacting massively parallel and large-scale workflow models.

• Journal of Korean Academy of Nursing
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• v.36 no.4
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• pp.652-661
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• 2006

Purpose. The purposes of this study were to apply data mining tool to nursing specific knowledge discovery process and to identify the utilization of data mining skill for clinical decision making. Methods. Data mining based on rough set model was conducted on a large clinical data set containing NMDS elements. Randomized 1000 patient data were selected from year 1998 database which had at least one of the five most frequently used nursing diagnoses. Patient characteristics and care service characteristics including nursing diagnoses, interventions and outcomes were analyzed to derive the meaningful decision rules. Results. Number of comorbidity, marital status, nursing diagnosis related to risk for infection and nursing intervention related to infection protection, and discharge status were the predictors that could determine the length of stay. Four variables (age, impaired skin integrity, pain, and discharge status) were identified as valuable predictors for nursing outcome, relived pain. Five variables (age, pain, potential for infection, marital status, and primary disease) were identified as important predictors for mortality. Conclusions. This study demonstrated the utilization of data mining method through a large data set with stan dardized language format to identify the contribution of nursing care to patient's health.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.5
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• pp.442-450
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• 2014

Technology opportunity discovery (TOD) based on technological capability is a process which identifies new product and technology items that can be developed by utilizing or improving a firm's existing products or technologies. By taking into consideration the investment risk of R&D and its practicality, developing technological capability-based TOD methodology is considered to be important for both business and research. To this end, we propose a technological capability-based TOD method and its system using TOD knowledge base. The method can support four types of TOD cases, which are based on a firm's existing technologies and products, and TOD knowledge base is developed by using function information extracted from patent documents. In this paper, we introduce the overall framework of the method and provide application examples on the four TOD cases using the prototype system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.12
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• pp.1134-1149
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• 2013

Pub/sub system enables data users to access any necessary data without knowledge of the data producer and synchronization with the data producer. It is widely used as the middleware technology for the data-centric services. DDS (Data Distribution Service) is a standard middleware supported by the OMG (Object Management Group), one of global standardization organizations. It is considered quite useful as a standard middleware for US military services. However, it is well-known that it takes considerably long time in searching the Participants and Endpoints in the system, especially when the system is booting up. In this paper, we propose a discovery scheme to reduce the latency when the participants and Endpoints are densely distributed in a small area. We propose to modify the standard DDS discovery process in three folds. First, we integrate the Endpoint discovery process with the Participant discovery process. Second, we reduce the number of connections per participant during the discovery process by adopting the concept of successors in Distributed Hashing scheme. Third, instead of UDP, the participants are connected through TCP to exploit the reliable delivery feature of TCP. We evaluated the performance of our scheme by comparing with the standard DDS discovery process. The evaluation results show that our scheme achieves quite lower discovery latency in case that the Participants and the Endpoints are densely distributed in a local network.

• Journal of Engineering Education Research
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• v.15 no.4
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• pp.41-47
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• 2012

Data mining and knowledge discovery techniques have shown to be effective in finding hidden underlying rules inside large database in an automated fashion. On the other hand, analyzing, assessing, and applying students' survey data are very important in science and engineering education because of various reasons such as quality improvement, engineering design process, innovative education, etc. Among those surveys, analyzing the students' views on science-technology-society can be helpful to engineering education. Because, although most researches on the philosophy of science have shown that science is one of the most difficult concepts to define precisely, it is still important to have an eye on science, pseudo-science, and scientific misconducts. In this paper, we report the experimental results of applying decision tree induction algorithms for analyzing the questionnaire results of high school students' views on science-technology-society (HS-VOSTS). Empirical results on various settings of decision tree induction on HS-VOSTS results from one South Korean university students indicate that decision tree induction algorithms can be successfully and effectively applied to automated knowledge discovery from students' survey data.

• Journal of The Korean Association For Science Education
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• v.33 no.1
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• pp.30-45
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• 2013

In this study, we developed a model for analyzing scientists' creative thinking processes, and analyzed Archimedes' thinking process in solving the golden crown problem. As results show, scientists' complex problem solving processes could be represented as a repeating circular model, and the fusion of processes of diverse thinking required for scientists' creativity could be analyzed from the case. Also in this study, we represented the role of experiments in scientists' creative discovery, and investigated the reasons for the difference between the viewpoints of textbooks and historic facts. We found the importance of abductive reasoning and advance knowledge in creative thinking. Archimedes solved the golden crown problem creatively by crossing the scientific thought of dynamics and the daily thought of baths. In this process, abductive reasoning and advance knowledge played an important role. Besides Archimedes' case, if we would reconstruct the creative discovery processes of diverse scientists' in textbooks, students could raise their creative thinking ability by experiencing these processes as educational steps.

• Interdisciplinary Bio Central
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• v.2 no.4
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• pp.9.1-9.5
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• 2010

Many neurodegenerative diseases, such as Alzheimer's and Parkinson's disease, are devastating disorders that affect millions of people worldwide. However, the number of therapeutic options remains severely limited with only symptomatic management therapies available. With the better understanding of the pathogenesis of neurodegenerative diseases, discovery efforts for disease-modifying drugs have increased dramatically in recent years. However, the process of translating basic science discovery into novel therapies is still lagging behind for various reasons. The task of finding new effective drugs targeting central nervous system (CNS) has unique challenges due to blood-brain barrier (BBB). Furthermore, the relatively slow progress of neurodegenerative disorders create another level of difficulty, as clinical trials must be carried out for an extended period of time. This review is intended to provide molecular and cell biologists with working knowledge and resources on CNS drug discovery and development.