• 제목/요약/키워드: cluster identification

검색결과 254건 처리시간 0.024초

Construction of Probability Identification Matrix and Selective Medium for Acidophilic Actinomycetes Using Numerical Classification Data

  • Seong, Chi-Nam;Park, Seok-Kyu;Michael Goodfellow;Kim, Seung-Bum;Hah, Yung-Chil
    • Journal of Microbiology
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    • 제33권2호
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    • pp.95-102
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    • 1995
  • A probability identification matrix of acidophilic Streptomyces was constructed. The phenetic data of the strains were derived from numerical classification described by Seong et al. The minimum number of diagnostic characters was determined using computer programs for calculation of different separation indices. The resulting matrix consisted of 25 clusters versus 53 characters. Theoretical evaluation of this matrix was achieved by estimating the chuster overlap and the identification scores for the Hypothetical Median Organisms (HMO) and for the representatives of each cluster. Cluster overlap was found to be relatively small. Identification scores for the HMO and the randomly selected representatives of each cluster were satisfactory. The matrix was assessed practically by applying the matrix to the identification of unknown isolates. Of the unknown isolates, 71.9% were clearly identified to one of eight clusters. The numerical classification data was also used to design a selective isolation medium for antibiotic-producing organisms. Four chemical substances including 2 antibiotics were determined by the DLACHAR program as diagnostic for the isolation of target organisms which have antimicrobial activity against Micrococcus luteus. It was possible to detect the increased rate of selective isolation on the synthesized medium. Theresults show that the numerical phenetic data can be applied to a variety of purposes, such as construction of identification matrix and selective isolation medium for acidophilic antinomycetes.

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국부 퍼지 클러스터링 PCA를 갖는 GMM을 이용한 화자 식별 (Speaker Identification Using GMM Based on Local Fuzzy PCA)

  • 이기용
    • 음성과학
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    • 제10권4호
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    • pp.159-166
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    • 2003
  • To reduce the high dimensionality required for training of feature vectors in speaker identification, we propose an efficient GMM based on local PCA with Fuzzy clustering. The proposed method firstly partitions the data space into several disjoint clusters by fuzzy clustering, and then performs PCA using the fuzzy covariance matrix in each cluster. Finally, the GMM for speaker is obtained from the transformed feature vectors with reduced dimension in each cluster. Compared to the conventional GMM with diagonal covariance matrix, the proposed method needs less storage and shows faster result, under the same performance.

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The Use of AFLP Markers for Cultivar Identification in Hydrangea macrophylla

  • Lee, Jae Ho;Hyun, Jung Oh
    • 한국산림과학회지
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    • 제96권2호
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    • pp.125-130
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    • 2007
  • The principal morphological characters used for identification of hydrangea cultivars are often dependent on agroclimatic conditions. Furthermore, information on the selection or the genetic background of the hydrangea breeding is so rare that a molecular marker system for cultivar identification is needed. Amplified fragment length polymorphism (AFLP) markers were employed for fingerprinting Hydrangea macrophylla cultivars and candidate cultivars of H. macrophylla selected in Korea. One AFLP primer combination was sufficient to distinguish 17 H. macrophylla cultivars and 4 candidate cultivars. The profile of 19 loci that can minimize the error of amplification peak detection was constructed. AFLP markers were efficient for identification, estimation of genetic distances between cultivars, and cultivar discrimination. Based on the observed AFLP markers, genetic relationship was reconstructed by the UPGMA method. Seventeen H. macrophylla cultivars and H. macrophylla for. normalis formed a major cluster, and candidate cultivars selected in Korea formed another cluster.

경계 차감 클러스터링에 기반한 클러스터 개수 추정 화자식별 (Speaker Identification with Estimating the Number of Cluster Based on Boundary Subtractive Clustering)

  • 이윤정;최민정;서창우;한헌수
    • 한국음향학회지
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    • 제26권5호
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    • pp.199-206
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    • 2007
  • 본 논문에서는 화자식별을 위한 특징벡터의 새로운 클러스터링 방법을 제안한다. 제안된 방법은 클러스터 센터에 대한 초기값 설정과 클러스터 개수에 대한 사전 정보 없이 클러스터링이 가능하다. 각 클러스터 센터는 경계 차감 클러스터링 알고리즘으로 한 번에 한 개의 클러스터 센터가 추가됨으로써 순차적으로 구해지며, 클러스터 개수는 클러스터간의 상호관계를 조사하여 결정된다. 인공 생성 데이터 및 TIMIT 음성을 이용하여 실험한 결과로부터 제안된 방법이 기존의 방법보다 우수함을 확인하였다.

SPATIAL DISTRIBUTION OF THE SPIN VECTORS OF THE DISK GALAXIES IN THE VIRGO CLUSTER

  • YUAN Q. R.;HU F. X.;HE X. T.
    • 천문학회지
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    • 제29권spc1호
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    • pp.55-56
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    • 1996
  • In order to investigate the spatial orientation of the spin vectors of galaxies in the Virgo cluster, we carried out a detailed identification of all the certain and possible member disk galaxies with four UK Schmidt Telescope (UKST) III a-j direct plates digitized by the Automated Plate Measuring System (APM). As a result, a relatively large and complete database with no selection effect of the member galaxies has been established. We provide the APM measured values of the position angle (P.A.) and diameters at the isophotal level of 24.5 $m_j / arcsec^2$. Based on this newly generated database, an initial study on the spatial orientation of the spin vectors of galaxies in the Virgo cluster is shown.

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Variable Selection and Outlier Detection for Automated K-means Clustering

  • Kim, Sung-Soo
    • Communications for Statistical Applications and Methods
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    • 제22권1호
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    • pp.55-67
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    • 2015
  • An important problem in cluster analysis is the selection of variables that define cluster structure that also eliminate noisy variables that mask cluster structure; in addition, outlier detection is a fundamental task for cluster analysis. Here we provide an automated K-means clustering process combined with variable selection and outlier identification. The Automated K-means clustering procedure consists of three processes: (i) automatically calculating the cluster number and initial cluster center whenever a new variable is added, (ii) identifying outliers for each cluster depending on used variables, (iii) selecting variables defining cluster structure in a forward manner. To select variables, we applied VS-KM (variable-selection heuristic for K-means clustering) procedure (Brusco and Cradit, 2001). To identify outliers, we used a hybrid approach combining a clustering based approach and distance based approach. Simulation results indicate that the proposed automated K-means clustering procedure is effective to select variables and identify outliers. The implemented R program can be obtained at http://www.knou.ac.kr/~sskim/SVOKmeans.r.

지역산업클러스터 사례연구 : 클러스터 평가지표와 정책과제 (A Case Study of Regional Industry Clusters : Clusters Estimate Index and Policy)

  • 원구현
    • 산학경영연구
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    • 제18권2호
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    • pp.197-223
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    • 2005
  • 21세기 산업 클러스터 정책이 지역경제 활성화의 핵심 수단으로 부상하면서 정책 대상으로서의 클러스터 도출(identification) 및 확인(mapping) 연구의 중요성이 부각되고 있다. 본 연구에서는 지역단위의 클러스터에 대한 사례연구를 통하여 클러스터 지향적 정책과제(focused cluster policy)의 도출하고, 지역산업 클러스터의 네트워크를 파악함에 있어서 타 클러스터와의 비교 및 시계열별 비교, 정책목표의 달성정도 평가를 위하여 클러스터 평가지표를 제시하고자 하였다. 산업클러스터 사례분석은 클러스터 유형화분석과 네트워크분석을 통해 이루어졌다. 연구결과 요약하면, 클러스터를 개발하려는 시도는 다른 지역에서 성공한 클러스터를 모방하기보다는 경쟁우위와 전문성을 토대로 추진되어야 하고, 산업정책과 다른 산업클러스터 정책을 개발해야하고, 시장지향적인 클러스터 생태계를 구축해야 하며, 각 지역 여건 및 산업특성에 적합한 혁신체제를 구축과 혁신창출의 인프라를 조성해야 한다는 것이다.

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Fuzzy Technique-based Identification of Close and Distant Clusters in Clustering

  • Lee, Kyung-Mi;Lee, Keon-Myung
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • 제11권3호
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    • pp.165-170
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    • 2011
  • Due to advances in hardware performance, user-friendly interfaces are becoming one of the major concerns in information systems. Linguistic conversation is a very natural way of human communications. Fuzzy techniques have been employed to liaison the discrepancy between the qualitative linguistic terms and quantitative computerized data. This paper deals with linguistic queries using clustering results on data sets, which are intended to retrieve the close clusters or distant clusters from the clustering results. In order to support such queries, a fuzzy technique-based method is proposed. The method introduces distance membership functions, namely, close and distant membership functions which transform the metric distance between two objects into the degree of closeness or farness, respectively. In order to measure the degree of closeness or farness between two clusters, both cluster closeness measure and cluster farness measure which incorporate distance membership function and cluster memberships are considered. For the flexibility of clustering, fuzzy clusters are assumed to be formed. This allows us to linguistically query close or distant clusters by constructing fuzzy relation based on the measures.

Analysis of cellular fatty acid methyl esters (FAMEs) for the identification of leuconostoc strains isolated from kimchi

  • Lee, Jung-Sook;Chun, Chang-Ouk;Kim, Hong-Joong;Joo, Yun-Jung;Lee, Hun-Joo;Park, Chan-Sum;Park, Yong-Ha;Mheen, Tae-Ick
    • Journal of Microbiology
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    • 제34권3호
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    • pp.225-228
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    • 1996
  • The cellular fatty acid methyl esters (FAMEs) analysis data obtained for clusters defined at a Euclidian distance of 17.5, in the classification of lactic acid bacteria isolated from kimchi, described by Lee et al. (4), was used for the identification of 79 Leuconostoc isolates. The test strains were isolated using a selective isolation medium specific for the genus Leuconostoc. These strains were then characterized according to their fatty acid profiles. The results show that all seventy nine test strains were identified to the known Leuconostoc clusters B, C, and D. Cluster B had the highest relative amount of the saturated fatty acid 16 : 0. The saturated fatty acid 16 : 0 and summed feature 9 were found as a major components in cluster C, which had a higher level of summed feature 9 than cluster B. Cluster D is characterized by the highest relative amount of the unsaturated fatty acid 18 : 1 w9c. It is suggested that FAMEs analysis can be successfully applied in the identification of lactic acid bacteria isolated from kimchi.

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온라인 진화형 TSK 퍼지 식별 (Online Evolving TSK fuzzy identification)

  • 김경중;박창우;김은태;박민용
    • 한국지능시스템학회논문지
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    • 제15권2호
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    • pp.204-210
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    • 2005
  • 본 논문에서는 TSK 퍼지 모델을 위한 온라인 식별 알고리즘을 제안한다. 제안된 알고리즘은 거리를 이용하여 TSK 퍼지 모델에 대한 전건부의 구조를 식별하고, 재귀적 최소자승법으로 후건부를 구성하는 부분 선형 함수들의 매개 변수를 구한다. 대부분의 다른 연구들에서는 전건부의 구조를 구하기 위해서 클러스터링을 수행할 때 입력 공간에서만 고려하였으나. 제안된 알고리즘에서는 입력 공간 및 출력 공간 모두에서 고려하여, 아웃라이어를 효과적으로 배제할 수 있다. 기존의 대부분의 다른 알고리즘에서 샘플 데이터자체를 클러스터의 중심으로 사용하여 잡음에 민감한 단점이 있었으나, 제안된 알고리즘에서는 데이터 자체를 클러스터의 중심으로 사용하지 않아 잡음에 대해 민감하지 않다. 제안된 알고리즘은 많은 데이터의 저장을 필요로 하지 않고, 한 번 통과함으로써 모델을 구할 수 있다.