• The Journal of Society for e-Business Studies
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• v.8 no.2
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• pp.77-98
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• 2003

A web-based information system, that is a dominant type of information systems, suffers from the "web crisis" in development and maintenance of the system. To cope with the problem, a technology of software clustering to web-based application, which is one of web engineering, is strongly needed. In this paper, we propose a Form Clustering Algorithm along with an application example, which are used for internal-system reengineering to web-based information system. A Form Clustering Algorithm focuses on Page-model which is the feature of the web among the various web-based information system's structural model. Specially, we applying distance matrix to navigation model of graph form for easily analyzing, and web log analysis for identifying core function object that have a highly loading. Also, we create web software structure that can be used to maximize reusability and assign hardware effectively through 2-phase clustering step. Form Clustering Algorithm might be used at web-based information system development and maintenance for reusable web component development and hardware assignment, respectively.

• Journal of KIISE:Software and Applications
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• v.26 no.12
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• pp.1468-1481
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• 1999

didates.본 논문은 절차 중심 소프트웨어를 객체 지향 소프트웨어로 재/역공학하기 위한 다단계 절차중 첫 절차인 객체 추출 절차에 대하여 기술한다. 사용한 객체 추출 방법은 전처리, 기본 분할 및 결합, 정제 결합, 결정 및 통합의 다섯 단계로 이루어진다 : 1) 전처리 과정에서는 객체 추출을 위한 FTV(Function, Type, Variable) 그래프를 생성/분할 및 클러스터링하고, 2) 기본 분할 및 결합 단계에서는 다중 객체 추출을 위한 그래프를 생성하고 생성된 그래프의 정적 객체를 추출하며, 3) 정제 결합 단계에서는 동적 객체를 추출하며, 4) 결정 단계에서는 영역 모델링과 다중 객체 후보군과의 유사도를 측정하여 영역 전문가가 하나의 최적합 후보를 선택할 수 있는 측정 결과를 제시하며, 5) 통합 단계에서는 전처리 과정에서 분리된 그래프가 여러 개 존재할 경우 각각의 처리된 그래프를 통합한다. 본 논문에서는 클러스터링 순서가 고정된 결정론적 방법을 사용하였으며, 가능한 경우의 수에 따른 다중 객체 후보, 객관적이고 의미가 있는 객체 추출 방법으로의 정제와 결정, 영역 모델링을 통한 의미적 관점에 기초한 방법 등을 사용한다. 이러한 방법을 사용함으로써 전문가는 객체 추출 단계에서 좀더 다양하고 객관적인 선택을 할 수 있다.Abstract This paper presents an object extraction process, which is the first phase of a methodology to transform procedural software to object-oriented software. The process consists of five steps: the preliminary, basic clustering & inclusion, refinement, decision and integration. In the preliminary step, FTV(Function, Type, Variable) graph for object extraction is created, divided and clustered. In the clustering & inclusion step, multiple graphs for static object candidate groups are generated. In the refinement step, each graph is refined to determine dynamic object candidate groups. In the decision step, the best candidate group is determined based on the highest similarity to class group modeled from domain engineering. In the final step, the best group is integrated with the domain model. The paper presents a new clustering method based on static clustering steps, possible object candidate grouping cases based on abstraction concept, a new refinement algorithm, a similarity algorithm for multiple n object and m classes, etc. This process provides reengineering experts an comprehensive and integrated environment to select the best or optimal object candidates.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.1
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• pp.45-50
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• 2019

With the gradual advancement of IT, passive work processes are automated and the overall quality of life has greatly improved. This is made possible by the formation of an organic topology between a wide variety of real-life smart devices. To serve these diverse smart devices, businesses or users are using the cloud. The services in the cloud are divided into Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). SaaS runs on PaaS, and PaaS runs on IaaS. Since IaaS is the basis of all services, an algorithm is required to operate virtualization resources efficiently. Among them, desktop resource virtualization is used for resource high availability of unused state time of existing desktop PC. Clustering of hierarchical structures is important for high availability of these resources. In addition, it is very important to select a suitable algorithm because many clustering algorithms are mainly used depending on the distribution ratio and environment of the desktop PC. If various attempts are made to find an algorithm suitable for desktop resource virtualization in an operating environment, a great deal of power, time, and manpower will be incurred. Therefore, this paper proposes a resource clustering simulator for cluster selection of desktop virtualization. This provides a clustering simulation to properly select clustering algorithms and apply elements in different environments of desktop PCs.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.10
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• pp.544-555
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• 2003

Since the result data from DNA microarray experiments contain a lot of gene expression information, adequate analysis methods are required. Hierarchical clustering is widely used for analysis of gene expression profiles. In this paper, we study leaf-ordering, which is a post-processing for the dendrograms output by hierarchical clusterings to improve the efficiency of DNA microarray data analysis. At first, we analyze existing leaf-ordering algorithms and then present new approaches for leaf-ordering. And we introduce a software HCLO(Hierarchical Clustering ＆ Leaf-Ordering Tool) that is our implementation of hierarchical clustering, some of existing leaf-ordering algorithms and those presented in this paper.

• International journal of advanced smart convergence
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• v.8 no.3
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• pp.46-53
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• 2019

When a partitioned structure is derived from a data set using a clustering algorithm, it is not unusual to have a different set of outcomes when it runs with a different order of data. This problem is known as the order bias problem. Many algorithms in machine learning fields try to achieve optimized result from available training and test data. Optimization is determined by an evaluation function which has also a tendency toward a certain goal. It is inevitable to have a tendency in the evaluation function both for efficiency and for consistency in the result. But its preference for a specific goal in the evaluation function may sometimes lead to unfavorable consequences in the final result of the clustering. To overcome this bias problems, the first clustering process proceeds to construct an initial partition. The initial partition is expected to imply the possible range in the number of final clusters. We apply the data centric sorting to the data objects in the clusters of the partition to rearrange them in a new order. The same clustering procedure is reapplied to the newly arranged data set to build a new partition. We have developed an algorithm that reduces bias effect resulting from how data is fed into the algorithm. Experiment results have been presented to show that the algorithm helps minimize the order bias effects. We have also shown that the current evaluation measure used for the clustering algorithm is biased toward favoring a smaller number of clusters and a larger size of clusters as a result.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.4
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• pp.2370-2375
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• 2014

Wireless sensor networks (WSNs) have a big potential for distributed sensing for large geographical area. The improvement of the lifetime of WSNs is the important research topic because it is considered to be difficult to change batteries of sensor nodes. Clustering communication protocols are energy-efficient because each sensor node can send its packet to the cluster head near from itself rather than the sink far from itself. In this paper, we present an energy-efficient clustering communication protocol, which chooses cluster heads based on the expected residual lifetime of each sensor node. Simulation results show that our proposed scheme increases average lifetimes of sensor nodes as much as 20% to 30% in terms of the traffic quantity and as much as 30% to 40% in terms of the scalability compared to the existing clustering communication protocol, LEACH.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.5
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• pp.496-505
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• 2000

Clusters of workstations (COW) are becoming an attractive option for parallel scientific computing, a field formerly reserved to the MPPs, because their cost-performance ratio is usuallybetter than that of comparable MPPS, and their hardware and software can be easily enhanced to thelatest generations. In this paper we present the design and implementation of our runtime library forclusters of workstations, called "Collective I/O Clustering". The library provides a friendlyprogramming model for the I/O of irregular applications on clusters of workstations, being completelyintegrated with the underlying communication and I/O system. In the collective I/O clustering, two I/Oconfigurations are possible. In the first I/O configuration, all processors allocated can act as I/Oservers as well as compute nodes. In the second I/O configuration, only a subset of processors canact as I/O servers, The compression and software caching facilities have been incorporated into thecollective 1/0 clustering to optimize the communication and I/O costs. All the performance results wereobtained on the IBM-SP machine, located at Argonne National Labs.

• Journal of Internet Computing and Services
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• v.23 no.6
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• pp.71-78
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• 2022

This paper proposes an algorithm for 3D object detection by processing point cloud data of 3D LiDAR. Unlike 2D LiDAR, 3D LiDAR-based data was too vast and difficult to process in three dimensions. This paper introduces various studies based on 3D LiDAR and describes 3D LiDAR data processing. In this study, we propose a method of processing data of 3D LiDAR using clustering techniques for object detection and design an algorithm that fuses with cameras for clear and accurate 3D object detection. In addition, we study models for clustering 3D LiDAR-based data and study hyperparameter values according to models. When clustering 3D LiDAR-based data, the DBSCAN algorithm showed the most accurate results, and the hyperparameter values of DBSCAN were compared and analyzed. This study will be helpful for object detection research using 3D LiDAR in the future.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.195-196
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• 2018

본 논문에서는 IIoT(Industrial IoT) 환경에서 사용되는 각 종 센서의 특성을 고려하여 K-means clustering을 이용해 측정 주기에 따른 군집화를 통해 평준화함으로써 센서에서 게이트웨이로의 데이터 전송 시 일어날 수 있는 1:1 독점 통신 현상 및 작업부하를 해결 할 수 있는 기법을 제안한다. 본 논문에서는 해당 기법의 효율을 보다 극대화할 수 있는 분산 게이트웨이 환경에서 실험을 진행하였으며, 해당 실험의 결과에 따르면 분산 게이트웨이 시스템에서 사용되는 게이트웨이들의 작업부하가 현저히 낮아졌고 각 종 센서들이 할당되는 빈도수가 일정하게 나타남으로써 신뢰성과 정확성을 확보에 보다 우수함을 보인다.

• Korean Journal of Remote Sensing
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• v.16 no.2
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• pp.145-156
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• 2000

Clustering is the process of partitioning a data set into meaningful clusters. As the data to process increase, a laster algorithm is required than ever. In this paper, we propose a clustering algorithm to partition a multispectral remotely sensed image data set into several clusters using a hash search algorithm. The processing time of our algorithm is compared with that of clusters algorithm using other speed-up concepts. The experiment results are compared with respect to the number of bands, the number of clusters and the size of data. It is also showed that the processing time of our algorithm is shorter than that of cluster algorithms using other speed-up concepts when the size of data is relatively large.