• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.1
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• pp.12-23
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• 2003

The expansion of internet and the development of communication technology have brought about an explosive increasement of data. Further progress has led to the increasing demand for efficient and effective data analysis tools. According to this demand, data mining techniques have been developed to find out knowledge from a huge amounts of raw data. This paper suggests a generalization based classification method which explores rules from real world data appearing repeatedly. Also, it analyzed the relation between weather data and forest fire, and efficiently predicted through it as a prediction model by applying the suggested generalization based classification method to forest fire data. Additionally, the proposed method can be utilized variously in the important field of real life like the analysis and prediction on natural disaster occurring repeatedly, the prediction of energy demand and so forth.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.2
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• pp.65-72
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• 2012

In the 21st century of information systems, the amount of information resources are ever increasing and the role of information searching system is becoming criticalto easily acquire required information from the web. Generally, it requires the user to have enough pre-knowledge and superior capabilities to identify keywords of information to effectively search the web. However, most of the users undertake searching of the information without holding enough pre-knowledge and spend a lot of time associating key words which are related to their required information. Furthermore, many search engines support the keywords searching system but this only provides collection of similar words, and do not provide the user with exact relational search information with the keywords. Therefore this research report proposes a method of offering expanded user relationship search keywords by analyzing user query patterns to provide the user a system, which conveniently support their searching of the information.

• Journal of the Korea Society of Computer and Information
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• v.19 no.12
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• pp.161-169
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• 2014

Digital forensics is the process of proving criminal charges by collecting and analyzing digital evidence which is related to the crime in question. Most digital forensic research is focused on digital forensic techniques themselves or cyber crime. In this paper, we designed a digital forensics-criminal investigation linked model in order to effectively apply digital forensics to various types of criminal investigations. Digital forensic ontology was developed based on this model. For more effective application of digital forensics to criminal investigation we derived specific application fields. The ontology has legality rules and adequacy rules, so it can support investigative decision-making. The ontology can be developed into an intelligent criminal investigation system.

• Annual Conference on Human and Language Technology
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• 2012.10a
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• pp.40-44
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• 2012

어휘가 갖는 의미적 중의성은 자연어의 특성 중 하나로 자연어 처리의 정확도를 떨어트리는 요인으로, 이러한 중의성을 해소하기 위해 언어적 규칙과 다양한 기계 학습 모델을 이용한 연구가 지속되고 있다. 의미적 중의성을 가지고 있는 동형이의어의 의미분별을 위해서는 주변 문맥이 가장 중요한 자질이 되며, 자질 정보를 추출하기 위해 사용하는 문맥 창의 크기는 중의성 해소의 성능과 밀접한 연관이 있어 신중히 결정되어야 한다. 본 논문에서는 의미분별과정에 필요한 문맥을 가변적인 크기로 사용하는 가변길이 윈도우 방식을 제안한다. 세종코퍼스의 형태의미분석 말뭉치로 학습하여 12단어 32,735문장에 대해 실험한 결과 용언의 경우 평균 정확도 92.2%로 윈도우를 고정적으로 사용한 경우에 비해 향상된 결과를 보였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.155-159
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• 2007

S/W 개발 프로젝트의 품질, 비용, 개발 기간을 잘 관리하여 프로젝트를 성공시키기 위해 PM 은 프로젝트의 종료시점과 예산의 초과를 예측할 수 있어야 한다. PMBOK 의 비용관리의 Earned Value Method 는 프로젝트의 진행에 따른 생산성의 변화와 그에 따른 비용과 일정의 증가 추정에 대한 규칙을 제시한다. 그러나 EVM 은 제조공정에서는 그 효과를 증명하였지만, S/W 프로젝트에서는 적용이 힘들어 잘 활용되고 있지 않다. 이는 사람이 주요 자원인 S/W 프로젝트에서는 Actual Cost 의 측정이 쉽지 않기 때문이다. 따라서 본 논문에서는 S/W 프로젝트 관리에서 Earned Value 의 측정이 쉽지 않아 추정되기 힘든 지연된 종료 시점에 대한 예측을 PMBOK 과 CMMI 에서 제시하는 관리 영역과의 연관성을 활용하고, EVM 을 보완하여 지연에 대한 예측모델을 만들어보고자 한다.

• The KIPS Transactions:PartD
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• v.14D no.1 s.111
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• pp.9-20
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• 2007

Groups of genes control the functioning of a cell by complex interactions. Such interactions of gene groups are tailed Gene Regulatory Networks(GRNs). Two previous data mining approaches, clustering and classification, have been used to analyze gene expression data. Though these mining tools are useful for determining membership of genes by homology, they don't identify the regulatory relationships among genes found in the same class of molecular actions. Furthermore, we need to understand the mechanism of how genes relate and how they regulate one another. In order to detect regulatory relationships among genes from time-series Microarray data, we propose a novel approach using frequent pattern mining and chain rules. In this approach, we propose a method for transforming gene expression data to make suitable for frequent pattern mining, and gene expression patterns we detected by applying the FP-growth algorithm. Next, we construct a gene regulatory network from frequent gene patterns using chain rules. Finally, we validate our proposed method through our experimental results, which are consistent with published results.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1125-1128
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• 2013

본 논문에서는 여성들의 개별 패션 선호도로부터 패션 선호 패턴을 분석하고 이를 이용하여 고객에게 맞는 의류를 추천하는 모바일 의류 추천 시스템을 제안한다. 패선 선호관련 설문조사로부터 대응표본 T-검정 방법을 이용하여 선호 특성과 의류와의 유효한 관계를 찾고, 이를 바탕으로 선호 특성에 따른 의류 분류 기준을 작성하였으며, 카이제곱 검정 방법을 통해 선호 특성과 의류 사이의 연관성을 파악하고 선호 특성에 따른 선호 의류 추천을 위한 규칙을 도출하였다. 이러한 규칙을 활용하여 각 사용자의 구입의사 및 패선 선호 특성에 따른 의류를 추천해 주는 시스템을 구현하였으며, 이에 대한 만족도를 조사한 결과 10 점 만점에 7.1 점으로 나타났다. 본 논문에서 제안한 모바일 의류 추천 시스템을 통해 사용자는 선호 의류를 추천 받을 수 있으며, 이로부터 제품의 정보 부족으로 발생하였던 모바일 쇼핑의 문제점을 해결할 수 있을 것이다.

• Journal of Service Research and Studies
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• v.5 no.1
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• pp.105-111
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• 2015

According to Korea Social Trends 2012 report presented in National Statistical Office, based on 2010, single-person household out of all households in Korea ratio is 23.9%, not only this ratio is beyond a family of four's ratio (22.5%) but also overtake couple-person household. Last year, according to financial industry and National Statistical Office, Korea's single-person household is estimated 4 million Five hundred and thirty thousand nine thousand family (25.3%). this mean is Korea's One of four household furniture is single-person household. Furthermore. According to National Statistical Office's report 'Future household projections 2010~2035 Report', In 2035, Korea's single-person household is assumed to increase by 34.3%. Korea's causes an increase of single-person household causes an increase is reduced marriage, increase in divorce, low fertility, increasing older singles etc. also Around the World as well as Korea single person household is increase. Based on 2011, single-person household is reached at 2 hundred million 42 million furniture (This ratio is 13%), China and U.S.A's single-person household ratio close in upon 30%. Sweden and Norway, the Philippines, Denmark is also approximately 40% of all households. Up to now, Not reached at OECD average, but this is increasing at a very fast pace. and then It will overtake this ratio. so government, regarding single-person household upsurge, try to find definitive solution. Appeared to statistics through the data, this find out the single-person household characteristics. Using association rule, the association between consumption trend and single-person.

• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.1-6
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• 2020

In this study, we propose an association inference model based on data bias optimization for road hazard detection. This is a mining model based on association analysis to collect user's personal characteristics and surrounding environment data and provide traffic accident prevention services. This creates transaction data composed of various context variables. Based on the generated information, a meaningful correlation of variables in each transaction is derived through correlation pattern analysis. Considering the bias of classified categorical data, pruning is performed with optimized support and reliability values. Based on the extracted high-level association rules, a risk detection model for personal characteristics and driving road conditions is provided to users. This enables traffic services that overcome the data bias problem and prevent potential road accidents by considering the association between data. In the performance evaluation, the proposed method is excellently evaluated as 0.778 in accuracy and 0.743 in the Kappa coefficient.

• Journal of Digital Convergence
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• v.14 no.11
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• pp.267-273
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• 2016

Creation and Sharing of information which is structured data as well as various unstructured data. makes progress actively through the spread of mobile. Recently, Big Data extracts the semantic information from SNS and data mining is one of the big data technique. Especially, the general emotion analysis that expresses the collective intelligence of the masses is utilized using large and a variety of materials. In this paper, we propose the emotion prediction system architecture which extracts the significant keywords from social network paragraphs using n-gram and Korean morphological analyzer, and predicts the emotion using SVM and these extracted emotion features. The proposed system showed 82.25% more improved recall rate in average than previous systems and it will help extract the semantic keyword using morphological analysis.