• Journal of the Korea Computer Industry Society
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• v.4 no.4
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• pp.479-490
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• 2003

CBD is a software development approach based on reusable component and supports easy modification and evolution of software. For the success of this approach, a component must be developed with high cohesion and low coupling. In this paper, we propose the two types of clustering analysis technique based on affinity between use-cases and classes and propose component identification method applying to this technique. We also propose component reference model and CBD methodology framework and perform a ease study to demonstrate how the affinity-based clustering technique is used in component identification method. Component identification method contains three tasks such as component extraction, component specification and component architecting. This method uses object-oriented concept for identifying component, which improves traceability from analysis to implementation and can automatically extract component. This method reflects the low coupling-high cohesion principle for good modularization about reusable component.

• The Transactions of the Korea Information Processing Society
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• v.6 no.6
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• pp.1571-1580
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• 1999

When a hierarchical structure is derived from data set for data mining and machine learning, using a conceptual clustering algorithm, one of the unsupervised learning paradigms, it is not unusual to have a different set of outcomes with respect to the order of processing data objects. To overcome this problem, the first classification process is proceeded to construct an initial partition. The partition is expected to imply the possible range in the number of final classes. We apply center sorting to the data objects in the classes of the partition for new data ordering and build a new partition using ITERATE clustering procedure. We developed an algorithm, REIT that leads to the final partition with stable and best partition score. A number of experiments were performed to show the minimization of order bias effects using the algorithm.

• Journal of IKEEE
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• v.24 no.1
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• pp.127-131
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• 2020

Fuzzy C-Means(FCM) algorithm is frequently used as a representative image segmentation method using clustering. FCM divides the image space into cluster regions with similar pixel values, which requires a lot of segmentation time. In particular, the processing speed problem for analyzing various patterns of the current users of the web is more important. To solve this speed problem, this paper proposes an improved FCM (Improved FCM : IFCM) algorithm for segmenting the image into the Otsu threshold and FCM. In the proposed method, the threshold that maximizes the variance between classes of Otsu is determined, applied to the FCM, and the image is segmented. Experiments show that IFCM improves performance by shortening image segmentation time compared to conventional FCM.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.1
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• pp.59-65
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• 2005

This paper proposes a novel TSK fuzzy modeling algorithm. Various approaches to fuzzy modeling when noise or outliers exist in the data have been presented but they are approaches to degrade effects of outliers or large noise by using loss function in the cost function mainly. The proposed algorithm is the modified version of noise clustering algorithm, and it adopts the method that does not use loss function, but method to cluster noise in a class. Noise clustering is a prototype-based clustering algorithm and it has no capability to regress. It conducts clustering of data first, and then conducts fuzzy regression. There are many algorithms to obtain parameters of premise and consequent part simultaneously, but they need to adapt the parameters obtained for more accurate approximation. In this paper, fuzzy regression is conducted with clustering by modifying noise clustering algorithm. We propose the algorithm that parameters of the premise part and the consequent part are obtained simultaneously, and the parameters obtained are not needed to adapt. We verify the proposed algorithm through simple examples and evaluate the test results compared with existing algorithms. The proposed algorithm shows robust performance against noise and it is easy to implement.

• Journal of KIISE:Databases
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• v.31 no.3
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• pp.287-296
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• 2004

Recently, various location-based services are becoming very popular in mobile environments. In this paper, we propose a new concept of a frequent item set, called “associative class set”, for supporting the location-based service which uses a large quantity of a spatial database in mobile computing environments, and then present a new method for efficiently generating the associative class set. The associative class set is generated with considering the temporal relation of queries, the spatial distance of required objects, and access patterns of users. The result of our research can play a fundamental role in efficiently supporting location-based services and in overcoming the limitation of mobile environments. The associative class set can be applied by a recommendation system of a geographic information system in mobile computing environments, mobile advertisement, city development planning, and client cache police of mobile users.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.1
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• pp.1-18
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• 2023

Similar software is often developed with the Clone-And-Own (CAO) approach that copies and modifies existing artifacts. The CAO approach is considered as a bad practice because it makes maintenance difficult as the number of cloned products increases. Software product line engineering is a methodology that can solve the issue of the CAO approach by developing a product family through systematic reuse. Migrating product families that have been developed with the CAO approach to the product line engineering begins with finding, integrating, and building them as reusable assets. However, cloning occurs at various levels from directories to code lines, and their structures can be changed. This makes it difficult to build product line code base simply by finding clones. Successful migration thus requires unifying the source code's file path, class name, and method signature. This paper proposes a clustering method that identifies a set of similar codes scattered across product variants and some of their method full paths are different, so path unification is necessary. In order to show the effectiveness of the proposed method, we conducted an experiment using the Apo Games product line, which has evolved with the CAO approach. As a result, the average precision of clustering performed without preprocessing was 0.91 and the number of identified common clusters was 0, whereas our method showed 0.98 and 15 respectively.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09b
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• pp.177-180
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• 2003

텔레비젼 카메라, 비디콘 카메라(vidicon camera), 디지털 검지기, 스캐너 등 물리적 장치로 획득한 영상은 주위의 밝기로 인하여 어두운 영상을 얻거나 영상장치의 물리적 속성과 영상 전송에 기인하여 영상은 열악한 대비를 가질 수 있다. 본 논문에서는 획득한 저대비 영상을 대비 향상시켜주는 기법을 제안한다. 제안된 기법은 K-means 알고리듬을 사용하여 교차점을 자동으로 선정하는 방법을 사용한다. 이 최적의 교차점을 선정하는 과정은 획득한 영상을 물체와 배경으로 분리하는 두 개의 클래스 문제로 보고 K-means 알고리듬을 적용하였다. 구한 교차점을 사용하여 영상을 양분하여 히스토그램 평활화 방법을 적용하였다. 본 논문에서는 퍼지성 지수(index of fuzziness)를 사용하여 향상의 정도를 측정하였다. 제안된 기법을 저대비 영상에 적용하였으며 그 결과를 히스토그램 평활화 기법의 결과와 비교하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.188-191
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• 2004

야간에 비디오카메라로 촬영시 열악한 주위 환경과 영상 전송에 기인하여 다양한 잡음에 의하여 왜곡되거나 흐린 저대비(low contrast)영상을 가질 수 있다. 본 논문에서는 획득한 저대비 영상을 대비 향상시켜주는 기법을 제안한다. 동영상 압축표준인 MPEG-2는 인간의 시각 특성상 색차(chrominance)신호보다 밝기(luminance)신호에 더 민감하기 때문에 밝기신호와 색차 신호를 분리하여 압축한다. 밝기신호만을 추출한 후 K-means 알고리듬을 사용하여 교차점을 자동으로 선정하는 방법을 사용하는데, 이 최적의 교차점을 선정하는 과정은 획득한 영상을 물체와 배경으로 분리하는 두 개의 클래스 문제로 보고 K-means 알고리듬을 적용하였고 구한 교차점을 사용하여 영상을 양분하여 히스토그램 평활화 방법을 적용하였다 븐 논문에서는 퍼지성 지수(index of fuzziness)를 사용하여 향상의 정도를 측정하였다. 제안된 기법을 저대비 영상에 적용하였으며 그 결과를 히스토그램 평활화 기법의 결과와 비교하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.244-246
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• 2005

상호작용하는 구조들을 하나의 클래스로 표현하는 데이터 마이닝 툴로서 이머징 패턴(EP)이 최근에 제안되었다. 기존의 클러스터링 알고리즘과 패턴 마이닝 알고리즘은 고차원의 유전자 발현 데이터 흑은 같은 변수들(e.g. genes)을 가지고 실험한 멀티 소스 데이터 분석을 다루기에 부적절하고, 실험 결과를 이해하는 데에 어려움이 있다. 그러나 EP는 분류 트리의 형태로 표현 가능하기 때문에, 다양한 형식의 데이터를 분류하는 패턴들을 빠르고 간단하게 구성하여 데이터 분석이 가능하도록 돕는다. 본 논문에서는 멀티 소스 바이오 데이터에서 분류 절차의 작업을 향상시키기 위하여 EP를 사용하는 간단한 스킴을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.428-430
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• 2020

이상 진단은 주어진 데이터의 정상 유무를 진단하는 방법으로써 다양한 분야에 걸쳐 요구되는 기능이다. 이상 진단은 대상 환경에서 발생하는 데이터의 특성 등에 따라 다양한 방법으로 구현이 될 수 있는데, 본 연구에서는 정상 데이터가 다수의 클래스로 구분될 수 있는 상황에서의 이상 진단을 효과적으로 할 수 있는 방법에 대해서 다루고자 한다. 특히, 실험을 통해 정상 데이터를 유사한 데이터들끼리 구분하여 처리하는 경우와 그렇지 않은 경우의 비교를 통해서, 정상 데이터를 유사한 데이터들끼리 구분하여 이상 진단을 진행하는 방법의 타당성을 검증한다.