• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.45
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• pp.101-112
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• 1998

Data mining, which is also referred to as knowledge discovery in database, means a process of nontrivial extraction of implicit, previously unknown and potentially useful information (such as knowledge rules, constraints, regularities) from data in databases. The discovered knowledge can be applied to information management, decision making, and many other applications. In this paper, a new data mining problem, discovering sequential patterns, is proposed which is to find all sequential patterns using sampling method. Recognizing that the quantity of database is growing exponentially and transaction database is frequently updated, sampling method is a fast algorithm reducing time and cost while extracting the trend of customer behavior. This method analyzes the fraction of database but can in general lead to results of a very high degree of accuracy. The relaxation factor, as well as the sample size, can be properly adjusted so as to improve the result accuracy while minimizing the corresponding execution time. The superiority of the proposed algorithm will be shown through analyzing accuracy and efficiency by comparing with Apriori All algorithm.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.475-477
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• 2005

본 연구는 신문기사나 백과사전 등의 문서에서 빈번히 발생하는 동사 파생 접미사와 어미가 생략된 형태의 서술성 명사를 동사로 복원하는 방법에 대한 것으로 이러한 복원은 문장구조 분석에 영향을 미친다. 기존 연구는 간단한 규칙만을 사용하지만 규칙을 사용하는 방법은 재현률에서 성능 저하를 보이기 때문에 본 연구에서는 이러한 생략 형태를 구분하여 규칙과 통계 방법을 사용하여 각각 적합한 형태에 적용하였다. 본 연구의 접근 방법은 규칙 기반에 비해 약 $30\%$, 통계 기반에 비해 약 $8\%$의 성능 향상을 보여서 문장 구조 분석에서는 $3.6\%$의 성능 향상을 보였다.

• Journal of Ship and Ocean Technology
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• v.12 no.1
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• pp.28-34
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• 2008

The purpose of this paper is to define the concept of KAD/KAM, furthermore is to establish my own idea on the knowledge related works in engineering domain for a next decade ahead. KAD/KAM is represented as "Knowledge Everywhere" based on the concept of ubiquitous computing in engineering domain. At the beginning of the paper, the definition of KAD/KAM is described. And the related technologies to realize KAD/KAM, such as augmented reality, ontology, data mining, and knowledge management, are introduced. The concept of KAD/KAM is still immature. But this will be a new paradigm to change entire engineering environment in near future.

• KSCI Review
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• v.8 no.2
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• pp.67-74
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• 2001

Recent times have seen an explosive growth in the availability of various kinds of data. It has resulted in an unprecedented opportunity to develop automated data-driven techniques of extracting useful knowledge. Data mining. an important step in this process of knowledge discovery consists of methods that discover interesting. non-trivial and useful Patterns hidden in the data In this paper. we surveyed data mining techniques. We find effective data mining techniques in applying real world. and suggest appropriate application area for the each techniques. We conclude the Paper with some research issues.

• Management Science and Financial Engineering
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• v.15 no.2
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• pp.101-118
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• 2009

In quantitative contexts, data mining is widely applied to the prediction of stock prices from financial time-series. However, few studies have examined the potential of data mining for shedding light on the qualitative problem-solving knowledge of experts who make stock price predictions. This paper presents a GA-based data mining approach to characterizing the qualitative knowledge of such experts, based on their observed predictions. This study is the first of its kind in the GA literature. The results indicate that this approach generates rules with higher accuracy and greater coverage than inductive learning methods or neural networks. They also indicate considerable agreement between the GA method and expert problem-solving approaches. Therefore, the proposed method offers a suitable tool for eliciting and representing expert decision rules, and thus constitutes an effective means of predicting the stock price index.

• International Journal of Knowledge Content Development & Technology
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• v.3 no.2
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• pp.43-65
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• 2013

In this work, we first introduce two main approaches to writing requirements and then propose a method based on Natural Language Processing to improve requirement authoring and the overall coherence, cohesion and organization of requirement documents. We investigate the structure of requirement kernels, and then the discourse structure associated with those kernels. This will then enable the system to accurately extract requirements and their related contexts from texts (called requirement mining). Finally, we relate a first experimentation on requirement mining based on texts from seven companies. An evaluation that compares those results with manually annotated corpora of documents is given to conclude.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.26 no.1
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• pp.217-231
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• 2015

This study aims to infer the gene-protein 'brings_about' chains of pancreatic cancer which were referred to in the pancreatic cancer related researches by constructing the gene-protein interaction network of pancreatic cancer. The chains can help us uncover publicly unknown knowledge that would develop as empirical studies for investigating the cause of pancreatic cancer. In this study, we applied a novel approach that grafts text mining and the main path analysis into Swanson's ABC model for expanding intermediate concepts to multi-levels and extracting the most significant path. We carried out text mining analysis on the full texts of the pancreatic cancer research papers published during the last ten-year period and extracted the gene-protein entities and relations. The 'brings_about' network was established with bio relations represented by bio verbs. We also applied main path analysis to the network. We found the main direct 'brings_about' path of pancreatic cancer which includes 14 nodes and 13 arcs. 9 arcs were confirmed as the actual relations emerged on the related researches while the other 4 arcs were arisen in the network transformation process for main path analysis. We believe that our approach to combining text mining analysis with main path analysis can be a useful tool for inferring undiscovered knowledge in the situation where either a starting or an ending point is unknown.

• Journal of KIISE:Databases
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• v.32 no.6
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• pp.567-584
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• 2005

Temporal data mining, the incorporation of temporal semantics to existing data mining techniques, refers to a set of techniques for discovering implicit and useful temporal knowledge from temporal data. Association rules and classification are applied to various applications which are the typical data mining problems. However, these approaches do not consider temporal attribute and have been pursued for discovering knowledge from static data although a large proportion of data contains temporal dimension. Also, data mining researches from temporal data treat problems for discovering knowledge from data stamped with time point and adding time constraint. Therefore, these do not consider temporal semantics and temporal relationships containing data. This paper suggests that temporal associative classification technique based on temporal class association rules. This temporal classification applies rules discovered by temporal class association rules which extends existing associative classification by containing temporal dimension for generating temporal classification rules. Therefore, this technique can discover more useful knowledge in compared with typical classification techniques.

• Journal of the Korea Society of Computer and Information
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• v.17 no.2
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• pp.139-147
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• 2012

In this paper, we discuss the structure of biological knowledge discovery system based on text mining and automatic inference. Given a set of biology documents, the system produces a new hypothesis in an integrated manner. The text mining module of the system first extracts the 'event' information of predefined types from the documents. The inference module then produces a new hypothesis based on the extracted results. Such an integrated system can use information more up-to-date and diverse than other automatic knowledge discovery systems use. However, for the success of such an integrated system, the precision of the text mining module becomes crucial, as any hypothesis based on a single piece of false positive information would highly likely be erroneous. In this paper, we propose a probabilistic filtering method that filters out false positives from the extraction results. Our proposed method shows higher performance over an occurrence-based baseline method.

• The Transactions of the Korea Information Processing Society
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• v.5 no.11
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• pp.2786-2796
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• 1998

Extraction of interesting and general knowledge from large spatial database is an important task in the development of geographical information system and knowledge-base systems. In this paper, we propose a spatial data mining system using generalization method; In this system, we extend an existing generalization mining and design a rulebase to support deriving new spatial knowledge. For this purpose, we propose an interleaved method which integrates spatial data dominated and nonspatial data dominated mining and construct a rulebase to extract topological relationship between spatial objects.