퓨전된 데이터 기반 잠재변수에 의한 연관성 규칙 적용 방안

조광현,박희창 한국자료분석학회 2008 Journal of the Korean Data Analysis Society Vol.10 No.1

현대 사회에서는 조직의 운영 및 의사 결정을 위하여 다양한 통계 조사가 실시되고 있으며, 연구의 목적에 따라 조사 문항을 다르게 하여 실시하고 있어 통계 분석 시 통계조사의 효율적 사용이 제한되어 있다. 이에 본 논문에서는 데이터 퓨전에 의해 생성된 자료에 대하여 잠재변수를 이용한 연관성 규칙을 적용하는 방안에 대하여 연구하고자 한다. 일반적으로 연관성 규칙 생성 시, 관심대상이 되는 항목이 많은 경우에는 규칙이 지나치게 많이 나와 규칙 분석의 어려움을 초래할 수 있다. 이와 같은 경우, 유사한 항목을 몇 개의 잠재 변수로 축소하여 연관성 규칙을 적용하면 연관성 규칙 도출 시간을 단축할 수 있고, 하나의 축약된 연관성 결과를 도출할 수 있어 더욱 효율적으로 분석을 실시할 수 있다. 이에 본 논문에서는 통계 조사 자료의 퓨전된 결과를 바탕으로 잠재변수에 의한 연관성 규칙 적용 방안인 하이브리드 데이터마이닝에 대하여 기술하고 예제 자료에 적용하여 그 유용성을 살펴보고자 한다. Today, the statistical survey has been carried out variously for the decision-making and administration of the organization. We use the different items in the statistical survey according to the purpose of study. Currently, gyeongnam province is executing the social index survey every year to the provincials. But, this survey has the limit of the analysis as execution of the different survey per 3 year cycles. The solution of this problem is data fusion. Data fusion is the process of combining multiple data in order to provide information of tactical value to the user. But, data fusion doesn't mean the ultimate result. Therefore, efficient analysis for the data fusion is also important. In this study, We present data fusion method of statistical survey data. Also, We suggest application methodology of association rule by latent variable through data fusion of statistical survey data.

MODIS 및 Landsat 위성영상의 다중 해상도 자료 융합 기반 토지 피복 분류의 사례 연구

김예슬,Kim, Yeseul 대한원격탐사학회 2022 大韓遠隔探査學會誌 Vol.38 No.6

This study evaluated the applicability of multi-resolution data fusion for land-cover classification. In the applicability evaluation, a spatial time-series geostatistical deconvolution/fusion model (STGDFM) was applied as a multi-resolution data fusion model. The study area was selected as some agricultural lands in Iowa State, United States. As input data for multi-resolution data fusion, Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat satellite images were used considering the landscape of study area. Based on this, synthetic Landsat images were generated at the missing date of Landsat images by applying STGDFM. Then, land-cover classification was performed using both the acquired Landsat images and the STGDFM fusion results as input data. In particular, to evaluate the applicability of multi-resolution data fusion, two classification results using only Landsat images and using both Landsat images and fusion results were compared and evaluated. As a result, in the classification result using only Landsat images, the mixed patterns were prominent in the corn and soybean cultivation areas, which are the main land-cover type in study area. In addition, the mixed patterns between land-cover types of vegetation such as hay and grain areas and grass areas were presented to be large. On the other hand, in the classification result using both Landsat images and fusion results, these mixed patterns between land-cover types of vegetation as well as corn and soybean were greatly alleviated. Due to this, the classification accuracy was improved by about 20%p in the classification result using both Landsat images and fusion results. It was considered that the missing of the Landsat images could be compensated for by reflecting the time-series spectral information of the MODIS images in the fusion results through STGDFM. This study confirmed that multi-resolution data fusion can be effectively applied to land-cover classification.

A Study of Association Rule Applicationusing Self-Organizing Map for Fused Data

조광현,박희창 한국데이터정보과학회 2008 한국데이터정보과학회지 Vol.19 No.1

Currently, Gyeongnam province is executing the social index survey to the provincials every year. But, this survey has the limit of the analysis as execution of the different survey per 3 year cycles. The solution for this problem is data fusion technique. Data fusion is the process of combining multiple data in order to provide information of tactical value to the user. Data fusion is generally defined as the use of techniques that collect to combine data including multiple sources in order to raise the quality of information. Data fusion is also called "data combination" or "data matching". Data fusion is divided into five branch types which are exact matching, judgemental matching, probability matching, statistical matching, and data linking. But, data fusion doesn't mean the ultimate result. Therefore, efficient analysis for the fused data is also important. In this study, we suggest the application methodology of association rule using SOM for the fused data of statistical survey data.

A Study of Association Rule Mining by Clustering through Data Fusion

조광현,박희창 한국데이터정보과학회 2007 한국데이터정보과학회지 Vol.18 No.4

Currently, Gyeongnam province is executing the social index survey every year to the provincials. But, this survey has the limit of the analysis as execution of the different survey per 3 year cycles. The solution of this problem is data fusion. Data fusion is the process of combining multiple data in order to provide information of tactical value to the user. But, data fusion doesn't mean the ultimate result. Therefore, efficient analysis for the data fusion is also important. In this study, we present data fusion method of statistical survey data. Also, we suggest application methodology of association rule mining by clustering through data fusion of statistical survey data.

A Study of Association Rule Mining by Clustering through Data Fusion

Cho, Kwang-Hyun,Park, Hee-Chang 한국데이터정보과학회 2007 한국데이터정보과학회지 Vol.18 No.4

Currently, Gyeongnam province is executing the social index survey every year to the provincials. But, this survey has the limit of the analysis as execution of the different survey per 3 year cycles. The solution of this problem is data fusion. Data fusion is the process of combining multiple data in order to provide information of tactical value to the user. But, data fusion doesn#t mean the ultimate result. Therefore, efficient analysis for the data fusion is also important. In this study, we present data fusion method of statistical survey data. Also, we suggest application methodology of association rule mining by clustering through data fusion of statistical survey data.

Street Fashion Information Analysis System Design Using Data Fusion

박혜원,박희창 한국데이터정보과학회 2005 한국데이터정보과학회지 Vol.16 No.4

Fashion is hard to expect owing to the rapid change in accordance with consumer taste and environment, and has a tendency toward variety and individuality. Especially street fashion of 21st century is not being regarded as one of the subcultures but is playing an important role as a fountainhead of fashion trend. Therefore, Searching and analyzing street fashions helps us to understand the popular fashions of the next season and also it is important in understanding the consumer fashion sense and commercial area. So, we need to understand fashion styles quantitatively and qualitatively by providing visual data and dividing images. There are many kinds of data in street fashion information. The purpose of this study is to design and implementation for street fashion information analysis system using data fusion. We can show visual information of customer's viewpoint because the system can analyze the fused data for image data and survey data.

공공데이터 융합의 영향요인과 매개효과에 관한 탐색적 연구: 데이터기반행정의 맥락을 중심으로

윤건(尹建),김윤희(金允熙) 한국지역정보화학회 2020 한국지역정보화학회지 Vol.23 No.3

최근 데이터기반행정 활성화에 관한 법률이 국회 본회의를 통과하면서 데이터에 기초해 보다 나은 행정서비스와 정책문제 해결을 지향하는 데이터기반행정 움직임이 구체화되고 있다. 이에 따라, 출처가 다른 데이터를 연계・결합함으로써 새롭고 창의적인 방식으로 정책문제 해결의 기회를 제공할 수 있는 데이터 융합의 중요성이 높아지고 있다. 본 연구는 데이터기반행정에 활용성이 높은 공공데이터를 중심으로 그 융합에 영향을 주는 요인들을 제도・역량・기술 측면에서 분류해 데이터 융합과의 관계 및 데이터기반행정과의 관계를 분석하고자 하였다. 이를 위해 공공데이터 실무 담당자와 국책연구기관 연구원을 대상으로 한 설문조사 자료를 연구에 이용하였다. 분석 결과, 공공데이터 융합에 통계적으로 유의미한 영향이 있는 요인들은 근거규정 미흡, 개인정보보호 문제, 데이터 표준화, 데이터 품질 등으로 나타났다. 또한, 공공데이터 융합은 역량 요인과 데이터기반행정을 연결하는 부분매개효과가 있는 것으로 확인되었다. 본 연구에서는 분석결과를 토대로 데이터기반행정 활성화를 위해서는 공공데이터 융합을 지속적으로 추진해야 하며, 융합에 영향을 주는 제도, 역량, 기술 요인들의 보완 및 관리 필요성을 제언하였다. The data-driven government movement has been much accelerated to provide better public services and resolve policy problems based on data after the passage of the Data Based Administration Promotion Act by the National Assembly in May 2020. The importance of data integration that can provide opportunities to solve policy issues in new and creative ways by using numerous sources of data has increased. Following this, this study purposed to examine the impact of data integration on data-driven government, using the survey data conducted to government administrators and researchers who experienced data integration projects. In detail, this study first analyzed the determinants of data integration categorized in the institutional, competency, and technological dimensions. Then, the next analysis tested a mediating effect of data integration on data-based government. The results found statistically significant effects of insufficient rules, privacy protection, data standardization, and data quality on the data integration status. Furthermore, data integration had a partial mediation effect to link competency factors and data-driven government. This study suggested that continuous efforts are needed to promote data integration practices to cultivate data-driven government and manage those institutional, competency, and technological factors.

디지털 기술과 인문학의 융합 ─ 데이터(data)활용을 중심으로

김호연 경기대학교 인문학연구소 2016 시민인문학 Vol.30 No.-

빅데이터를 활용하여 인문학의 새로운 정체성을 확보하려는 노력은 넓게 보아 디지털 기술과 인문학의 융합 시도로 그 의미가 크다. 이미 우리 시대의 트렌드가 된 융합은 거 스를 수 없는 것임에 틀림없다. 그러나 융합이 얼마나 실질적이 고 효과적으로 진행되고 있는지는 의견이 분분하다. 융합 열풍에 조응하는 통일 된 개념이나 진지한 논의의 부재 그리고 철학적 빈곤에 대한 우려도 존재한다. 따라서 우리는 융합적 시도의 안착과 이를 기초로 한 학문공동체의 성숙을 도모 하기 위해 융합이라는 용어에 대한 과도한 의미부여나 융합에 대한 이해타산적 접근에서 벗어나 융합을 둘러싼 다양한 문제들에 대해서 진지하게 숙고해봐야만 한다. 이런 견지에서 이 글에서는 융합이라는 용어와 유형의 다양성, 디지털 기술의 발전이 야기한 학문생태계의 지형도 변화와 인문학의 새로운 정체성 확보를 위 한 여러 시도들 그리고 빅데이터를 활용한 인문학의 활용에서 제기될 수 있는 위험요소 등을 살펴보았다. 빅데이터 인문학 또는 디지털 인문학 또는 인문학 적 정보학, 무엇으로 지칭하던 간에, 이는 디지털 기술과 인문학의 수평적인 관 계 속에서 새로운 영역을 창조하는 두 문화의 건설적인 융합 시도임에 틀림없 다. 성공적인 융합은 두 영역의 상호존중에 기초한 협업이 무엇보다 중요하고, 협업은 협동의 질에 달려 있으며, 협동의 질은 사람에 대한 이해의 정도가 좌우 할 것이다. An effort to establish identity of humanities utilizing big data has significant meaning as it can be interpreted as the fusion of humanities and digital technology. This fusion has already became trend in our time that we cannot ignore. There is various opinions about how much is this fusion actual and effective. There are also concerns about the unified concept corresponding to this fusion trend, about the lack of serious discussion, or about the poverty of philosophy. Therefore, in order to promote the maturity of scholastic community based on settlement and attempt of fusion, we should seriously consider various problems around fusion rather than attaching too much significance to the word“ fusion”or accessing in calculating point of view. From this point of view, this article mainly looked close to diversity of the word and types of fusion, transformation of academic map caused by the development of digital technology, numerous attempts to establish new identity of humanities, and risk factors that can be suggested in the field of humanities utilizing big data. It is a concrete fact that big data humanities, or some would say digital humanities or humanistic information science, is a very constructive fusion attempt of two culture creating new realm in horizontal relationship between digital technology and humanities. In order to achieve a successful fusion, collaboration based on mutual respect for each field is the most important factor, where collaboration depend upon the quality of cooperation, and finally, the quality of cooperation will be decided upon the understanding toward human being.

차세대 침입탐지에서 이상탐지를 위한 추론 기반 데이터 융합 알고리즘

김동욱(Dong-Wook Kim),한명묵(Myung-Mook Han) 한국지능시스템학회 2016 한국지능시스템학회논문지 Vol.26 No.3

본 논문은 차세대 침입탐지 시스템을 위해서 데이터 융합에서의 불확실한 데이터 처리의 알고리즘을 제안한다. 차세대 침입탐지는 사이버 공간에서 생성되어지는 정보를 지식으로 만들어내기 위해 수많은 네트워크 센서로부터의 데이터가 수집되어진다. 수집된 센서 정보를 지식의 수준으로 이끌어내기 위해서 데이터 융합의 과정이 필요하다. 이를 위해 본 논문에서는 Demster-Shafer 증거이론 추론적 기법을 통하여 서로 다른 데이터들의 특징을 분석하여 불확실한 데이터가 어느 구간에서 신뢰구간을 갖는지를 분류하여, 불확실한 데이터에 대한 표현을 이루어낸다. 본 실험내용에서는 이러한 불확실성 데이터에 대한 이상탐지를 위해 iris plant 데이터세트를 이용한 신뢰구간에 따른 분류를 실행하였다. 이에 대 해 각 신뢰구간을 통해서 데이터 분류가 가능하다는 것을 검증하였다. In this paper, we propose the algorithms of processing the uncertainty data using data fusion for the next generation intrusion detection. In the next generation intrusion detection, a lot of data are collected by many of network sensors to discover knowledge from generating information in cyber space. It is necessary the data fusion process to extract knowledge from collected sensors data. In this paper, we have proposed method to represent the uncertainty data, by classifying where is a confidence interval in interval of uncertainty data through feature analysis of different data using inference method with Dempster-Shafer Evidence Theory. In this paper, we have implemented a detection experiment that is classified by the confidence interval using IRIS plant Data Set for anomaly detection of uncertainty data. As a result, we found that it possible to classify data by confidence interval.

Fusion of probe and fixed sensor data for short-term traffic prediction in urban signalized arterials

Babak Mehran,Masao Kuwahara 서울시립대학교 도시과학연구원 2013 도시과학국제저널 Vol.17 No.2

A data fusion framework is proposed to predict vehicle trajectories in urban signalized arterials. Recent advancements in data collection techniques and availability of traffic data from fixed and probe sensors suggest data fusion-based models as an alternative approach for traffic prediction. Yet, majority of the existing data fusion approaches are based on statistical models without considerations for traffic engineering principles. In addition, existing approaches do not use probe trajectory data efficiently. The proposed framework in this research is based on the kinematic wave theory and is capable of fully utilizing the probe trajectory data to reconstruct the trajectories of the other vehicles within a ‘prediction window’. The data fusion framework combines real-time and historical traffic data to predict future traffic patterns at upstream and downstream boundaries. The modelling approach is based on the kinematic wave theory and applies the variational theory as the solution method. Predicted traffic patterns are used to set the boundary conditions in the solution domain and probe trajectories are used to set additional boundary conditions. Given the boundary conditions, a dynamic programming approach is applied to reconstruct vehicle trajectories within the prediction window. The performance of the proposed framework is evaluated by using real-world traffic data, and possible directions for improving the accuracy of the model are discussed.