• Digital Contents
• /
• 10호통권161호
• /
• pp.46-47
• /
• 2006

한국데이터베이스진흥센터가 주관하는 '2006 데이터베이스 그랜드 컨퍼런스'가 최근 부각되고 있는 데이터 거버넌스를 주제로 지난달 20일 코엑스 인터컨티넨탈 호텔에서 개최됐다. 이번 행사는 기업들이 당면하고 있는 데이터 품질 확보를 위한 해법으로 데이터 거버넌스를 제안, 관련 분야의 해외 전문가들을 초청하여 선진 기술동향을 파악할 수 있는 계기가 됐다는 평가다.

• Journal of the Korean Society for information Management
• /
• 제29권2호
• /
• pp.45-70
• /
• 2012

There have been many methods and algorithms proposed for multidimensional scaling to mapping the relationships between data objects into low dimensional space. But traditional techniques, such as PROXSCAL or ALSCAL, were found not effective for visualizing the proximities between objects and the structure of clusters of large data sets have more than 50 objects. The CLUSCAL(CLUster-oriented SCALing) technique introduced in this paper differs from them especially in that it uses cluster structure of input data set. The CLUSCAL procedure was tested and evaluated on two data sets, one is 50 authors co-citation data and the other is 85 words co-occurrence data. The results can be regarded as promising the usefulness of CLUSCAL method especially in identifying clusters on MDS maps.

• Journal of the Korea Society of Computer and Information
• /
• 제24권12호
• /
• pp.93-100
• /
• 2019

Similarity computation is very crucial to performance of memory-based collaborative filtering systems. These systems make use of user ratings to recommend products to customers in online commercial sites. For better recommendation, most similar users to the active user need to be selected for their references. There have been numerous similarity measures developed in literature, most of which suffer from data sparsity or cold start problems. This paper intends to extract preference information as much as possible from user ratings to compute more reliable similarity even in a sparse data condition, as compared to previous similarity measures. We propose a new similarity measure which relies not only on user ratings but also on movie genre information provided by the dataset. Performance experiments of the proposed measure and previous relevant measures are conducted to investigate their performance. As a result, it is found that the proposed measure yields better or comparable achievements in terms of major performance metrics.

• Journal of the Korea Society of Computer and Information
• /
• 제18권1호
• /
• pp.177-183
• /
• 2013

In this paper, Cohen's kappa and weighted kappa are applied to measuring classification accuracy when performing classification in data minig. Cohen's kappa compensates for classifications that may be due to chance and is used for the data with nominal or ordinal scales. Especially, for the ordinal data, weighted kappa which measures the classification accuracy by quantifying the classification errors as weights is used. We used two weights (linear weight, quadratic weight) for calculations of weighted kappa. Also for the calculation and comparison of kappa and weighted kappa we used a real data set, fat-liver data.

• Journal of Digital Convergence
• /
• 제13권11호
• /
• pp.157-164
• /
• 2015

This paper describes subspace clustering of categorical data for convergence and integration. Because categorical data are not designed for dealing only with numerical data, The conventional evaluation measures are more likely to have the limitations due to the absence of ordering and high dimensional data and scarcity of frequency. Hence, conditional entropy measure is proposed to evaluate close approximation of cohesion among attributes within each cluster. We propose a new objective function that is used to reflect the optimistic clustering so that the within-cluster dispersion is minimized and the between-cluster separation is enhanced. We performed experiments on five real-world datasets, comparing the performance of our algorithms with four algorithms, using three evaluation metrics: accuracy, f-measure and adjusted Rand index. According to the experiments, the proposed algorithm outperforms the algorithms that were considered int the evaluation, regarding the considered metrics.

• Proceedings of the Korean Information Science Society Conference
• /
• 한국정보과학회 2005년도 가을 학술발표논문집 Vol.32 No.2 (1)
• /
• pp.712-714
• /
• 2005

본 논문은 효율적인 점 기반 렌더링(Point-based Rendering)을 위해 확장 이차 오류 척도(Quadric Error Metrics) 기법을 이용하는 간략화 알고리즘을 제안한다. 점 기반 렌더링의 기본 개념은 자유곡면을 메쉬와 같은 연결정보 없이 직접 점들로 표현하고, 렌더링하는 것이다. 확장 이차 오류 척도 기법은 메쉬를 간략화 하는데 있어 기하 정보뿐만 아니라 색상, 텍스쳐 좌표 정보까지 고려하여 간략화 하는 알고리즘이다. 이 연구는 3차원 점 데이터로부터 복원한 폴리곤 메쉬 모델로부터 효율적인 점 기반 렌더링(Point-based Rendering)을 위해 기하 정보 및 색상 정보까지 고려하여 원본 점 데이터를 간략화 하는 저용량의 효율적인 점 기반 렌더링 알고리즘을 제안하고, GPU 기반 렌더링 결과를 보였다.

• Journal of the Korean BIBLIA Society for library and Information Science
• /
• 제14권1호
• /
• pp.79-90
• /
• 2003

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 한국퍼지및지능시스템학회 2005년도 춘계학술대회 학술발표 논문집 제15권 제1호
• /
• pp.166-169
• /
• 2005

질적인 속성을 양적인 계열로 전환하여 측정하는 방법으로서 설문의 최소 문항으로서 최대의 효과를 나타내기 위하여 한 항목을 k번 측정하여 평균과 $95\%$ 신뢰구간을 퍼지수로 한 데이터들을 리커트 척도로 활용하여 내분비방법에 의하여 검정을 하였다.

• Journal of the Korean Data and Information Science Society
• /
• 제27권2호
• /
• pp.373-380
• /
• 2016

Even though Likert type data is ordinal scale, many researchers who regard Likert type data as interval scale adapt as parametric methods. In this research, simulations have been used to find out a proper analysis of Likert type data. The locations and response distributions of five point Likert type data samples having diverse distribution have been evaluated. In estimating samples' locations, we considered parametric method and non-parametric method, which are t-test and Mann-Whitney test respectively. In addition, to test response distribution, we employed Chi-squared test and Kolmogorov-Smirnov test. In this study, we assessed the performance of the four aforementioned methods by comparing Type I error ratio and statistical power.

• Proceedings of the Korean Information Science Society Conference
• /
• 한국정보과학회 2001년도 가을 학술발표논문집 Vol.28 No.2 (1)
• /
• pp.481-483
• /
• 2001

클래스의 응집도를 측정하기 위한 여러 연구들이 제안되었지만, 이런 연구들은 데이터 상호작용에 의해 응집도를 측정하므로 메소들간에 데이터 상호작용이 없지만 객체의 또 다른 속성들을인 데이터들이 함께속하는 경우를 고려하지 못하고 있다. 따라서 본 연구에서는 데이터 상호작용이 없는 경우를 고려하고, 또한 클래스 내의 멤버들과 멤버들간의 연결을 모두 고려하여 응집도를 측정할 수 있는 새로운 응집도 척도를인 강 클래스 응집도(Strong Class Cohesion: SCC)와 약 클래스 응집도(Weak Class Cohesion: WCC)를 제안하였다. 또한 기존 척도들과의 비교평가를 통해서 WCC와 SCC가 향상된 측정을 제시함을 보여 주었다.