• 천문학회보
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• 제35권2호
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• pp.81.1-81.1
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• 2010

We present a study of five open clusters (Alessi 53, Berkeley 49, Berkeley 84, Czernik 5, Pfleiderer 3) based on ugriz images of the Sloan Digital Sky Survey (SDSS). Physical properties of these clusters are not yet well known. The center and size of these clusters are determined using the radial number density profile. Using the proper motion data, we select the members of the target clusters. We estimate physical parameters of the clusters based on the isochrone fitting in the Color-Magnitude Diagram (CMD) : reddening, distance, and age. The foreground reddening is determined to be E(B-V)=0.71-1.55 mag. The distances to target clusters are derived to be 2.0-4.4 kpc, corresponding to the galactocentric distances of 7.5-11.9 kpc. Their ages are in the range of 280 to 1000 Myr. Their spatial distribution in our Galaxy is similar to that of other intermediate-age open clusters. We find ten blue straggler star candidates in Berkeley 49. This number of blue stragglers is a typical value for the age of Berkeley 49.

• Journal of Ecology and Environment
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• 제36권1호
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• pp.11-17
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• 2013

The life cycle of butterflies is closely related to the growth of food plants and, through a prolonged coevolutionary process, has undergone an ecological adaptation. So, it is important that control the egg-laying number and place to secure enough food plant by female adult to guarantee the survival of the larvae. To study whether oviposition control of the Luehdorfia puziloi takes into greater consideration food plant leaf biomass or leaf abundance, correlation among the egg cluster size, the leaf size, and the number of leaves around egg clusters was investigated. According to the results, the egg clusters size exhibited positive correlation with the leaf size of food plants on which eggs had been laid but did not do so with the number of surrounding leaves. In addition, the number of egg clusters laid exhibited positive correlation with the number of surrounding food plant leaves but not with the leaf size on which eggs had been laid. Consequently, for the Luehdorfia puziloi, the females' recognition of the leaf size seems to be the most important factor in the egg cluster size, and the number of egg clusters had positive correlation with food plant density per unit area.

• 천문학회보
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• 제40권2호
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• pp.58.3-58.3
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• 2015

Blue Straggler Stars (BSSs), kind of unusual main sequence stars, are the brighter and bluer stars than the main sequence turn off (MSTS) stars in coeval clusters. Since the first detection in globular clusters (GCs), BSSs have been shown to reveal an anti-correlation between the luminosity of their host star cluster and the number of BSSs in the cluster. Further, conclusions based on this result can be expanded to the open clusters. BSSs seem to play an important role in GCs according to the relation between the dynamical time scale of GCs and the number of BSSs along the cluster radius. This relation, however, remains to be verified in open clusters, In this study, we divide open clusters by the existence of BSSs into two groups. Then we compare parameters between these groups to specify the role of BSSs in open clusters.

• Journal of the Korean Statistical Society
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• 제20권2호
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• pp.162-176
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• 1991

The problem of determining the number of clusters, K. is the main objective of this study. Attention is focused on the use of Rand(1971)'s $C_{k}$ statistic with some agglomerative clustering algorithms(ACA) defined in the ($\beta$, $\pi$) plane in predicting the number of clusters within the given set of data. The (k, $C_{k}$) plots for k=1, 2, …, N are explored by a Monte Carlo study. Based on its performance, the use of $C_{k}$ with the pair of ACA, (-.5, .75) and (-.25, .0), is recommended for predicting the number of clusters present within a set of data. data.

• 한국지능시스템학회논문지
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• 제14권7호
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• pp.816-820
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• 2004

클러스터링은 데이터 집합을 유사한 데이터 개체들의 클러스터들로 분할하여 데이터 속에 존재하는 의미 있는 정보를 얻는 과정이다. 클러스터링의 주요 쟁점은 고차원 데이터를 효율적으로 클러스터링하는 것과 최적화 문제를 해결하는 것이다. 본 논문에서는 SVM(Support Vector Machines)기반의 새로운 유사도 측정법과 효율적으로 클러스터의 개수를 생성하는 방법을 제안한다. 고차원의 데이터는 커널 함수를 이용해 Feature Space로 매핑시킨 후 이웃하는 클러스터와의 유사도를 측정한다. 이미 생성된 클러스터들은 측정된 유사도 값과 Δd 임계값에 의해서 원하는 클러스터의 개수를 얻을 수 있다. 제안된 방법을 검증하기 위하여 6개의 UCI Machine Learning Repository의 데이터를 사용한 결과, 제시된 클러스터의 개수와 기존의 연구와 비교하여 향상된 응집도를 얻을 수 있었다.

• 천문학회보
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• 제46권1호
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• pp.54.2-54.2
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• 2021

Galaxy clusters are the largest structures in the universe located at the top of the cosmological hierarchical model, so the evolution of the universe can be understood by studying clusters of galaxies. Therefore, finding a larger number of galaxy clusters plays an important role in exploring how the universe evolves. A large number of catalogs for galaxy clusters in the northern sky have been published; however, there are few catalogs in the southern sky due to the lack of wide sky survey data. KMTNet Synoptic Survey of Southern Sky(KS4) project, which observes a wide area of the southern sky about 7000 deg² with KMTNet telescopes for two years, is in progress under the SNU Astronomy Research Center. We use the KS4 multi-wavelength optical data and measure photometric redshifts of galaxies for finding galaxy clusters at redshift z<1. Currently, the KS4 project has observed approximately 33% of the target region, and a pipeline that measures photometric redshifts of galaxies has been created. When the project is completed, we expect to find more than a hundred thousand galaxy clusters, and this will improve the study of galaxy clusters in the southern sky.

• 한국경영과학회지
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• 제29권1호
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• pp.43-56
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• 2004

The purpose of clustering is to partition a set of objects into several clusters based on some appropriate similarity measure. In most cases, clustering is considered without any prior information on the number of clusters or the structure of the given data, which makes clustering is one example of very complicated combinatorial optimization problems. In this paper we propose a general-purpose clustering method that can determine the proper number of clusters as well as efficiently carry out clustering analysis for various types of data. The method is composed of two stages. In the first stage, two different hierarchical clustering methods are used to get a reasonably good clustering result, which is improved In the second stage by ASA(accelerated simulated annealing) algorithm equipped with specially designed perturbation schemes. Extensive experimental results are given to demonstrate the apparent usefulness of our ASA clustering method.

• 한국콘텐츠학회논문지
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• 제6권1호
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• pp.23-30
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• 2006

Temporal 데이터의 클러스터링 방법론 중의 하나로 모델기반 방법론이 있다. 이는 각 클러스터에 대하여 오토마타기반의 모델을 가정하는 것이다. 개별 모델을 추출하기 위해서는 먼저 전체 데이터에 대한 적합한 모델을 찾는 것이 필요하다. 전체에 대한 모델은 데이터집합에 대한 최적의 클러스터의 수를 결정함으로 개별 모델 구축의 준비를 완료한다. 본 연구에서는 클러스터 수를 결정하기 위한 기준인 베이지안 정보기준(BIC : Bayesian Information Criterion) 근사법의 활용도를 검증하고 데이터 크기와 BIC 값의 상관관계를 파악함으로 탐색 효율을 높이는 방안을 제안한다. 실험에서는 인위적 모델을 통하여 생성된 인공적인 여러 형태의 데이터집합을 활용하여 BIC근사 측도의 활용성에 대해 살펴보았다. 실험결과에서 보여주는 것처럼 BIC 근사 측도는 데이터의 크기가 비교적 클 경우에 올바른 파티션의 사이즈를 추정함을 확인하였다.

• Journal of Information Processing Systems
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• 제20권2호
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• pp.185-199
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• 2024

In this paper, an improved automated spectral clustering (IASC) algorithm is proposed to address the limitations of the traditional spectral clustering (TSC) algorithm, particularly its inability to automatically determine the number of clusters. Firstly, a cluster number evaluation factor based on the optimal clustering principle is proposed. By iterating through different k values, the value corresponding to the largest evaluation factor was selected as the first-rank number of clusters. Secondly, the IASC algorithm adopts a density-sensitive distance to measure the similarity between the sample points. This rendered a high similarity to the data distributed in the same high-density area. Thirdly, to improve clustering accuracy, the IASC algorithm uses the cosine angle classification method instead of K-means to classify the eigenvectors. Six algorithms-K-means, fuzzy C-means, TSC, EIGENGAP, DBSCAN, and density peak-were compared with the proposed algorithm on six datasets. The results show that the IASC algorithm not only automatically determines the number of clusters but also obtains better clustering accuracy on both synthetic and UCI datasets.

• 천문학회보
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• 제46권2호
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• pp.68.2-68.2
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• 2021

Galaxy clusters are the largest structures in the universe located at the top of the cosmological hierarchical model, so the evolution of the universe can be understood by studying clusters of galaxies. Therefore, finding a larger number of galaxy clusters plays an important role in exploring how the universe evolves. A large number of catalogs for galaxy clusters in the northern sky have been published; however, there are few catalogs in the southern sky due to the lack of wide sky survey data. KMTNet Synoptic Survey of Southern Sky(KS4) project, which observes a wide area of the southern sky about 7000 deg² with KMTNet telescopes for two years, is in progress under the SNU Astronomy Research Center. We use the KS4 multi-wavelength optical data and measure photometric redshifts of galaxies for finding galaxy clusters at redshift z<1. Currently, the KS4 project has observed approximately 50% of the target region, and a pipeline that measures photometric redshifts of galaxies has been created. When the project is completed, we expect to find more than a hundred thousand galaxy clusters, and this will improve the study of galaxy clusters in the southern sky.