• 한국컴퓨터정보학회논문지
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• 제15권4호
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• pp.11-18
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• 2010

Fuzzy c-means(FCM)는 퍼지 집합을 응용한 간단하지만 효율적인 클러스터링 방법 중 하나이다. FCM은 여러 응용 분야에서 성공적으로 활용되어 왔지만, 초기화와 잡음에 민감하고 볼록한 형태의 클러스터들만 다룰 수 있는 문제점이 있다. 이 논문에서는 이러한 FCM의 문제점을 해결하기 위해 전역 클러스터링(global clustering) 기법과 커널 클러스터링(kernel clustering) 기법을 결합하여 새로운 비선형 클러스터링 기법인 커널 전역 FCM(kernel global fuzzy c-means, KG-FCM)을 제안한다. 전역 클러스터링은 클러스터링의 초기화를 위한 방법 중 하나로, 순차적으로 클러스터를 하나씩 추가함으로써 초기화에 민감한 FCM의 한계를 극복할 수 있도록 해준다. FCM의 잡음 민감성과 볼록한 클러스터들만 다룰 수 있는 한계를 극복하기 위한 방법은 여러 가지가 있으며 커널 클러스터링이 그 중 하나이다. 커널 클러스터링은 사용하는 커널을 바꿈으로써 쉽게 확장이 가능하므로 이 논문에서는 커널 클러스터링을 사용하였다. 두 방법을 결합함으로써 제안한 방법은 위에서 언급한 문제점들을 해결할 수 있으며, 이는 가상 및 실제 데이터를 이용한 실험 결과를 통해 확인할 수 있다.

• 산업경영시스템학회지
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• 제40권2호
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• pp.92-98
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• 2017

Data clustering determines a group of patterns using similarity measure in a dataset and is one of the most important and difficult technique in data mining. Clustering can be formally considered as a particular kind of NP-hard grouping problem. K-means algorithm which is popular and efficient, is sensitive for initialization and has the possibility to be stuck in local optimum because of hill climbing clustering method. This method is also not computationally feasible in practice, especially for large datasets and large number of clusters. Therefore, we need a robust and efficient clustering algorithm to find the global optimum (not local optimum) especially when much data is collected from many IoT (Internet of Things) devices in these days. The objective of this paper is to propose new Hybrid Simulated Annealing (HSA) which is combined simulated annealing with K-means for non-hierarchical clustering of big data. Simulated annealing (SA) is useful for diversified search in large search space and K-means is useful for converged search in predetermined search space. Our proposed method can balance the intensification and diversification to find the global optimal solution in big data clustering. The performance of HSA is validated using Iris, Wine, Glass, and Vowel UCI machine learning repository datasets comparing to previous studies by experiment and analysis. Our proposed KSAK (K-means+SA+K-means) and SAK (SA+K-means) are better than KSA(K-means+SA), SA, and K-means in our simulations. Our method has significantly improved accuracy and efficiency to find the global optimal data clustering solution for complex, real time, and costly data mining process.

• 반도체디스플레이기술학회지
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• 제19권4호
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• pp.135-138
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• 2020

This paper suggests a method of automatic clustering to search of relevant digital photos using geo-coded information. The provided scheme labels photo images with their corresponding global positioning system coordinates and date/time at the moment of capture, and the labels are used as clustering metadata of the images when they are in the use of retrieval. Experimental results show that geo-location information can improve the accuracy of image retrieval, and the information embedded within the images are effective and precise on the image clustering.

• 통상정보연구
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• 제7권4호
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• pp.245-268
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• 2005

The purpose of this paper is to acquire competitiveness faced with a global business so that Korea and China make them put ICT into practice through industrial policy of regional innovation clustering. In the Chapter 2, overall review of industrial spaces theory and the environment in Global-business is conducted. In the Chapter 3, current main economic issue and West Great Development of China are viewed. Chapter 4 proposes models and strategies for the target of regional innovation clustering and phasing in development. The results of this study is that both country should do more long-term cooperation and collecting intensive knowledge for the property of region and preparatory research of regional innovation clustering than do reckless investment.

• 대한원격탐사학회지
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• 제34권1호
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• pp.151-166
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• 2018

This paper compares the four selections of performance used in the application of genetic algorithms (GAs) to automatically optimize multispectral pixel cluster for unsupervised classification from KOMPSAT-3 data, since the selection among three main types of operators including crossover and mutation is the driving force to determine the overall operations in the clustering GAs. Experimental results demonstrate that the tournament selection obtains a better performance than the other selections, especially for both the number of generation and the convergence rate. However, it is computationally more expensive than the elitism selection with the slowest convergence rate in the comparison, which has less probability of getting optimum cluster centers than the other selections. Both the ranked-based selection and the proportional roulette wheel selection show similar performance in the average Euclidean distance using the pixel clustering, even the ranked-based is computationally much more expensive than the proportional roulette. With respect to finding global optimum, the tournament selection has higher potential to reach the global optimum prior to the ranked-based selection which spends a lot of computational time in fitness smoothing. The tournament selection-based clustering GA is used to successfully classify the KOMPSAT-3 multispectral data achieving the sufficient the matic accuracy assessment (namely, the achieved Kappa coefficient value of 0.923).

• Journal of Computing Science and Engineering
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• 제10권1호
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• pp.9-20
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• 2016

Several task clustering heuristics are proposed for allocating tasks in heterogeneous systems to achieve a good response time in data intensive jobs. However, one of the challenging problems is the process in task scheduling after task allocation by task clustering. We propose a task scheduling method after task clustering, leveraging worst schedule length (WSL) as an upper bound of the schedule length. In our proposed method, a task in a WSL sequence is scheduled preferentially to make the WSL smaller. Experimental results by simulation show that the response time is improved in several task clustering heuristics. In particular, our proposed scheduling method with the task clustering outperforms conventional list-based task scheduling methods.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅲ
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• pp.1507-1510
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• 2003

We propose UPGMA(Unweighted Pair Group Method using Average distance) as hierarchical clustering to detect abrupt shot changes using multiple features such as pixel-by-pixel difference, global and local histogram difference. Conventional $\kappa$-means algorithm which is a method of the partitional clustering, has to select an efficient initial cluster center adaptively UPGMA that we propose, does not need initial cluster center because of agglomerative algorithm that it starts from each sample for clusters. And UPGMA results in stable performance. Experiment results show that the proposed algorithm works not only well but also stably.

• 한국멀티미디어학회논문지
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• 제20권8호
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• pp.1224-1235
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• 2017

Recently, augmented reality technology has received much attention in many fields. This paper presents an augmented reality system using Rubiks' Cube which can augment various 3D objects depending on patterns of a Rubiks' cube. The system first detects a cube from an image using partitional clustering and strongly connected graph. Thereafter, the system detects the top side of the cube and finds a proper pattern to determine which object should be augmented. An object corresponding to the pattern is finally augmented according to the camera viewpoint. Experimental results show that the proposed system successfully augments various virtual objects in real time.

• Journal of Information Processing Systems
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• 제7권1호
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• pp.17-28
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• 2011

Despite the fact that the deployment of sensor networks and target tracking could both be managed by taking full advantage of Voronoi diagrams, very little few have been made in this regard. In this paper, we designed an optimized barrier coverage and an energy-efficient clustering algorithm for forming Vonoroi-based Wireless Sensor Networks(WSN) in which we proposed a mobile target tracking scheme (CTT&MAV) that takes full advantage of Voronoi-diagram boundary to improve detectability. Simulations verified that CTT&MAV outperforms random walk, random waypoint, random direction and Gauss-Markov in terms of both the average hop distance that the mobile target moved before being detected and lower sensor death rate. Moreover, we demonstrate that our results are robust as realistic sensing models and also validate our observations through extensive simulations.

• Asian Pacific Journal of Cancer Prevention
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• 제16권9호
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• pp.4025-4029
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• 2015

Background: Gastrointestinal tract cancers are among the most common cancers in Iran and comprise approximately 38% of all the reported cases of cancer. This study aimed to describe the epidemiology and to investigate spatial clustering of common cancers of the gastrointestinal tract across the counties of Iran using full Bayesian smoothing and Moran I Index statistics. Materials and Methods: The data of the national registry cancer were used in this study. Besides, indirect standardized rates were calculated for 371 counties of Iranand smoothed using Winbug 1.4 software with a full Bayesian method. Global Moran I and local Moran I were also used to investigate clustering. Results: According to the results, 75,644 new cases of cancer were nationally registered in Iran among which 18,019 cases (23.8%) were esophagus, gastric, colorectal, and liver cancers. The results of Global Moran's I test were 0.60 (P=0.001), 0.47 (P=0.001), 0.29 (P=0.001), and 0.40 (P=0.001) for esophagus, gastric, colorectal, and liver cancers, respectively. This shows clustering of the four studied cancers in Iran at the national level. Conclusions: High level clustering of the cases was seen in northern, northwestern, western, and northeastern areas for esophagus, gastric, and colorectal cancers. Considering liver cancer, high clustering was observed in some counties in central, northeastern, and southern areas.