• 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.

• 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.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.125-133
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• 1999

The objective of knowledge discovery and data mining lies in the generation of useful insights from a store of data. This paper presents a framework for knowledge mining to provide a systematic approach to the selection and deployment of tools for automated learning. Every methodology has its strengths and limitations. Consequently, a multistrategy approach may be required to take advantage of the strengths of disparate technique while circumventing their individual limitations. For concreteness, the general framework for data mining in marketing is examined in the context of developing agents for optimizing a supply chain network.

• 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.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.125-133
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• 1999

The objective of knowledge discovery and data mining lies in the generation of useful insights from a store of data. This paper presents a framework for knowledge mining to provide a systematic approach to the selection and deployment of tools for automated learning. Every methodology has its strengths and limitations. Consequently, a multistrategy approach may be required to take advantage of the strengths of disparate technique while circumventing their individual limitations. For concreteness, the general framework for data mining in marketing is examined in the context of developing agents for optimizing a supply chain network.

• 한국디지털정책학회:학술대회논문집
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• 2004.05a
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• pp.575-592
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• 2004

In modern dynamic management environment, there is growing recognition that? information & knowledge management systems are essential for CEO's efficient/effective decision making. As a key component to cope with this current, we suggest the management performance analysis syystem based on Knowledge Discovery in Database (KDD). The system measures management performance that is considered with both VA(Value- Added), which represents stakeholder's point of view and EVA(Economic Value-Added), which represents shareholder's point of view. The relation ship between management performance and some 80 financial ratios is analyzed, and then important financial ratios are drawn out. In analyzing the relationship, we applied KDD process which includes such as multidimensional cube, OLAP(On-Line Analytic Process), data mining and AHP(Analytic Hierarchy Process). To demonstrate the performance of the system, we conducted a case study using financial data over the 16-years from 1981 to 1996 of Korean automobile industry which is taken from database of KISF AS(Korea Investors Services Financial Analysis System).

• 한국디지털정책학회:학술대회논문집
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• 2003.12a
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• pp.191-210
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• 2003

In modern dynamic management environment, there is growing recognition that information & knowledge management systems are essential for CEO's Efficient/effective decision making, As a key component to cope with this current, we suggest the management performance analysis system based on Knowledge Discovery in Database (KDD). The system measures management performance that is considered with both VA(Value-Added), which represents stakeholder's point of view and EVA(Economic Value-Added), which represents shareholder's point of view, The relationship between management performance and some 80 financial ratios is analyzed, and then important financial ratios are drawn out. In analyzing the relationship, we applied KDD process which includes such as multidimensional cube, OLAP(On -Line Analytic Process), data mining and AHP(Analytic Hierarchy Process). To demonstrate the performance of the system, we conducted a case study using financial data over the 16-years from 1981 to 1996 of Korean automobile industry which is taken from database of KISFAS(Korea Investors Services Financial Analysis System).

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.258-263
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• 2005

Haplotype-based analysis using high-density SNP markers have gained a great attention in evaluating genes in gene analysis and various clinical situations. However, there has been no research on disease diagnostic modeling based on SNPs analysis to our knowledge. The purpose of this study is to explore how knowledge discovery techniques are applied in medical informatics area and proposes a Case Based Reasoning (CBR) technique for diagnosis of gastric caner using Single Nucleotide Polymorphism(SNP).

• The KIPS Transactions:PartB
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• v.9B no.5
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• pp.635-642
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• 2002

There is a rapidly growing demand for the intellectualization of information technology. Especially, in the area of KDD (Knowledge Discovery in Database) which should make an optimal decision of finding knowledge from a large amount of data, the demand is enormous. A large volume of Knowledge Base should be efficiently managed for a more intellectual choice. This study is proposing a Hybrid SOM for an efficient search and renewal of knowledge base, which combines a self-study nerve network, Self-Organization Map with a probable distribution theory in order to get knowledge needed for decision-making management from the Knowledge Base. The efficient knowledge base management through this proposed method is carried out by a stimulation test. This test confirmed that the proposed Hybrid SOM can manage with efficiency Knowledge Base.

• Computers and Concrete
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• v.31 no.4
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• pp.277-292
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• 2023

Much of our experiments are designed to uncover the cause(s) and effect(s) behind a phenomenon (i.e., data generating mechanism) we happen to be interested in. Uncovering such relationships allows us to identify the true workings of a phenomenon and, most importantly, to realize and articulate a model to explore the phenomenon on hand and/or allow us to predict it accurately. Fundamentally, such models are likely to be derived via a causal approach (as opposed to an observational or empirical mean). In this approach, causal discovery is required to create a causal model, which can then be applied to infer the influence of interventions, and answer any hypothetical questions (i.e., in the form of What ifs? Etc.) that commonly used prediction- and statistical-based models may not be able to address. From this lens, this paper builds a case for causal discovery and causal inference and contrasts that against common machine learning approaches - all from a civil and structural engineering perspective. More specifically, this paper outlines the key principles of causality and the most commonly used algorithms and packages for causal discovery and causal inference. Finally, this paper also presents a series of examples and case studies of how causal concepts can be adopted for our domain.