• 한국해안·해양공학회논문집
• /
• 제30권1호
• /
• pp.19-28
• /
• 2018

서지정보학의 발달 및 전산화로 방대한 양의 연구논문들이 축적되고 있다. 이에 따라 전 세계에서 출판되는 관련 분야 논문들을 모두 검토하기는 실질적으로 어려워졌으며, 연구방향을 잡고 추진하는 것도 어려워졌다. 그러나 자연어 처리기법의 발달로 인해 출판된 연구논문들의 경향 분석이 수월해졌다. 여기서는 해안 해양공학 분야의 SCOPUS DB(Data Base) 서지정보 텍스트 마이닝(Text Mining) 분석을 R언어를 이용하여 수행했다. 분석 결과, 예상한 바와 같이 'wave' 용어가 압도적으로 우세하였으며, 'numerical model', 'numerical simulation' 및 experimental study' 용어로부터 여전히 수치해석 및 수리실험의 우세가 확인되었다. 또한 최근 해양에너지와 관련되는 'wave energy' 용어 사용이 부각되고 있는 것으로 파악되었다. 한편, 해안 해양공학 분야의 연구주제 용어의 빈도와 연결 관계는 'wave -> height, energy' 우세를 정량적으로 확인할 수 있었으며, 향후 세부분야 및 시기별 고해상도 분석 가능성을 제시하였다.

• 대한기계학회논문집 C: 기술과 교육
• /
• 제4권1호
• /
• pp.27-34
• /
• 2016

지열 시스템을 대상으로 데이터 마이닝 기반 성능 예측 모델을 구축하였다. 지열 시스템의 실시간 성능 분석과 예측에 필요한 데이터의 기본 조건을 검토한 후, 데이터베이스의 구조를 설계하였다. 먼저 시스템 성능계수(COP)와 전력 소비량을 분석 대상으로 설정한 후, 이들 물리량의 추출 주기(1분 5분 10분 30분 60분 간격)가 예측 결과에 미치는 영향을 분석하였다. 이어서 범주형과 수치형 의사결정나무 모델을 적용하여 시스템의 성능을 예측하였다. 범주형 의사결정나무 모델을 적용했을 때, 10분 주기의 예측 결과의 정확도는 97.7%로 가장 높았다. 또한 수치형 의사결정나무 분석 결과를 통해 COP가 변하는 순간의 임계값을 찾을 수 있었다. 본 논문에서 제안한 방법은 지열 시스템의 실시간 성능 분석과 운전 상태 등에 적용할 수 있을 것으로 판단된다.

• 한국의류산업학회지
• /
• 제24권5호
• /
• pp.545-556
• /
• 2022

To overcome the pandemic, a new strategy for innovation is in demand throughout the value chains of the fashion industry that emphasize the importance of fashion technology. Accordingly, as various viewpoints and fields of debate are unfolding to consider the direction of change led by fashion technology, it is necessary to make an active value judgment precedent by understanding the differences between various opinions. This study aims to derive keywords from fashion technology used during the pandemic, to infer the characteristics of each type of perspective and to understand their characteristics. For the research, this study combines text mining analysis and content analysis. Text mining analysis is used to find statistical patterns by collecting keywords from big data from online media, and content analysis is used to interpret the data qualitatively. After analyzing the results of this study, the following observations are made. First, the perspective of positive acceptance seeks to maximize the perception and sensory action of fashion through technology; this amplifies experience, an opportunity for innovation and efficiency. Second, critical vigilance highlights the side effects of radical changes in fashion technology, characterized by concerns about capital-centered polarization, threats to human rights, and infringement of creative thinking. Lastly, the perspective of gradual adoption is the gradual convergence of technologies, characterized by the pursuit of an appropriate balance.

• International journal of advanced smart convergence
• /
• 제9권4호
• /
• pp.124-131
• /
• 2020

As users of internet-based software applications increase, functional and non-functional problems for software applications are quickly exposed to user reviews. These user reviews are an important source of information for software improvement. User review mining has become an important topic of intelligent software engineering. This study proposes a user review mining method for software improvement. User review data collected by crawling on the app review page is analyzed to check user satisfaction. It analyzes the sentiment of positive and negative that users feel with a machine learning method. And it analyzes user requirement issues through topic analysis based on structural topic modeling. The user review mining process proposed in this study conducted a case study with the a non-face-to-face video conferencing app. Software improvement through user review mining contributes to the user lock-in effect and extending the life cycle of the software. The results of this study will contribute to providing insight on improvement not only for developers, but also for service operators and marketing.

• 디지털콘텐츠학회 논문지
• /
• 제16권4호
• /
• pp.545-553
• /
• 2015

모든 데이터는 그 자체로 가치를 가지고 있지만, 실세계에서 수집되는 데이터들은 무작위적이며 비구조화되어 있다. 따라서 이러한 데이터를 효율적으로 활용하기 위해서 데이터에서 유용한 정보를 추출하기 위한 데이터 변환과 분석 알고리즘들을 사용하게 된다. 이러한 목적으로 사용되는 것이 데이터 마이닝이다. 오늘날에는 데이터를 분석하기 위한 다양한 데이터 마이닝 기법뿐만 아니라, 대용량 데이터를 효율적으로 처리하기 위한 연산 요건과 빠른 분석 시간을 필요로 하고 있다. 대용량 데이터를 저장하기 위하여 하둡이 많이 사용되며, 이 하둡의 데이터를 분석하기 위하여 맵리듀스 프레임워크를 사용한다. 본 논문에서는 단일 머신에서 동작하는 알고리즘을 맵리듀스 프레임워크로 개발할 때 적용의 효율성을 높이기 위한 토너먼트 기반 적용 방안을 제안하였다. 본 방법은 다양한 알고리즘에 적용할 수 있으며, 널리 사용되는 데이터 마이닝 알고리즘인 k-means, k-근접 이웃 분류에 적용하여 그 유용성을 보였다.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제8권1호
• /
• pp.1-5
• /
• 2008

Generally the analytical tools of data mining have two learning types which are supervised and unsupervised learning algorithms. Classification and prediction are main analysis tools for supervised learning. In this paper, we perform a comparison study of classification algorithms in data mining. We make comparative studies between popular classification algorithms which are LDA, QDA, kernel method, K-nearest neighbor, naive Bayesian, SVM, and CART. Also, we use almost all classification data sets of UCI machine learning repository for our experiments. According to our results, we are able to select proper algorithms for given classification data sets.

• Journal of information and communication convergence engineering
• /
• 제1권3호
• /
• pp.150-156
• /
• 2003

This paper explored the use of an algorithm for the data mining and method in handling missing data which had generated enhanced association patterns observed using the data illustrated here. The evaluations showed that more association patterns are generated in the second analysis which suggests more meaningful rules than in the first situation. It showed that the model offer more precise and important association rules that is more valuable when applied for business decision making. With the discovery of accurate association rules or business patterns, strategies could be efficiently planned out and implemented to improve marketing schemes. This investigation gives rise to a number of interesting issues that could be explored further like the effect of outliers and missing data for detecting fraud and devious database entries.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2005년도 ICCAS
• /
• pp.2192-2197
• /
• 2005

This paper describes how to diagnose SBR plant equipment using time-series data mining. It shows the equipment diagnostics based upon vibration signals that are acquired from each device for process control. Data transform techniques including two data preprocessing skills and data mining methods were employed in the data analysis. The proposed method is not only suitable for SBR equipment, but is also suitable for other industrial devices. The experimental results performed on a lab-scale SBR plant show a good equipment-management performance.

• Journal of Korea Trade
• /
• 제24권8호
• /
• pp.171-188
• /
• 2020

Purpose - This is an exploratory study that aims to apply text mining techniques, which computationally extracts words from the large-scale text data, to legal documents to quantify trade claim contents and enables statistical analysis. Design/methodology - This is designed to verify the validity of the application of text mining techniques as a quantitative methodology for trade claim studies, that have relied mainly on a qualitative approach. The subjects are 81 cases of arbitration and court judgments from China published on the website of the UNCITRAL where the CISG was applied. Validation is performed by comparing the manually analyzed result with the automatically analyzed result. The manual analysis result is the cluster analysis wherein the researcher reads and codes the case. The automatic analysis result is an analysis applying text mining techniques to the result of the cluster analysis. Topic modeling and semantic network analysis are applied for the statistical approach. Findings - Results show that the results of cluster analysis and text mining results are consistent with each other and the internal validity is confirmed. And the degree centrality of words that play a key role in the topic is high as the between centrality of words that are useful for grasping the topic and the eigenvector centrality of the important words in the topic is high. This indicates that text mining techniques can be applied to research on content analysis of trade claims for statistical analysis. Originality/value - Firstly, the validity of the text mining technique in the study of trade claim cases is confirmed. Prior studies on trade claims have relied on traditional approach. Secondly, this study has an originality in that it is an attempt to quantitatively study the trade claim cases, whereas prior trade claim cases were mainly studied via qualitative methods. Lastly, this study shows that the use of the text mining can lower the barrier for acquiring information from a large amount of digitalized text.

• Journal of Information Technology Applications and Management
• /
• 제24권4호
• /
• pp.187-196
• /
• 2017

It is urgent to prepare countermeasures for traffic congestion problems of Korea's metropolitan area where central functions such as economic, social, cultural, and education are excessively concentrated. Most users of public transportation in metropolitan areas including Seoul use the traffic cards. If various information is extracted from traffic big data produced by the traffic cards, they can provide basic data for transport policies, land usages, or facility plans. Therefore, in this study, we extract valuable information such as the subway passengers' frequent travel patterns from the big traffic data provided by the Seoul Metropolitan Government Big Data Campus. For this, we use a Hadoop (High-Availability Distributed Object-Oriented Platform) to preprocess the big data and store it into a Mongo database in order to analyze it by a sequential pattern data mining technique. Since we analysis the actual big data, that is, the traffic cards' data provided by the Seoul Metropolitan Government Big Data Campus, the analyzed results can be used as an important referenced data when the Seoul government makes a plan about the metropolitan traffic policies.